The first axis of the EICCA DTU is the exploration of a possible unified framework for the measurement of EI with a detailed comparison of the theoretical properties of the different measures that have been proposed. As noted by Rohde and Tang (2018), EI is related to, but distinct from, traditional measures of individual and societal welfare such as poverty, relative deprivation, vulnerability, mobility, inequality, risk, and uncertainty. These differences will be analysed in detail, highlighting the links with multidimensional poverty, deprivation and risk. We will also analyse the role of formal and informal insurance, redistribution and risk-sharing as mechanisms capable of combating this phenomenon, as well as the spillover effects generated by social diversity and the degree of polarization and social cohesion.
We also aim to extend the empirical work on Australia in Rohde, D’Ambrosio and Watson (2022) to other countries for which long household panel data is available, such as Germany, Italy, the UK and the USA. Rohde et al. (2022) compare the results of many of the different measures of EI summarized above in Australia between 2002 and 2018. They show that the correlation between the indices is low, and that it is difficult to establish an unambiguous trend of EI over time. Nonetheless, all of the indices are negatively correlated with life satisfaction and individual health in Australia, after conditioning on standard factors such as current income and demographic characteristics. We believe that this unified study of the theoretical and empirical literature on insecurity, together with its comparison with more traditional measures of individual and societal wellbeing, is essential to provide a guide for researchers interested in the study of EI. It is currently not uncommon to find empirical articles on EI that use inappropriate measures. At the same time, this guide would assist policy makers and practitioners, more generally, to implement evidence-based effective actions to tackle insecurity. We will also aim to support technology transfer by providing computer software packages (Stata, R) to compute indices of EI, so as to make calculations straightforward for practitioners and researchers alike (building, e.g., on the expertise of one of the DTU supervisors, Van Kerm).

• There are two PhD positions in Economics or Social Sciences on topics related to the measurement of EI. These projects will cover the development of a comprehensive framework to measure EI, refining definitions and measurement methods

It is important to understand the consequences of EI for individuals and societies. The implications of EI are far-reaching and have been analysed in a number of contributions. EI has been shown to influence household decisions such as children’s educational choices (Stiglitz et al., 2009), career strategies (Chauvel and Schroeder 2014), family break-up (Larson et al., 1994), consumption patterns (Linz and Semykina, 2010), fertility (Ciganda, 2015; Clark and Lepinteur, 2022) and marriage (Clark, D’Ambrosio and Lepinteur, 2022). EI is also one of the causes of ill health (Lepinteur, 2021; D’Ambrosio et al., 2023), and in particular has been shown to affect cognitive functioning (Mani et al., 2013), self-esteem (Heine et al., 2006), psychological disorders (Menendez-Espina et al., 2019), and suicide risks (Chauvel et al., 2016). As stress and anxiety often manifest as physical symptoms, as observed by DeLongis et al. (1988), EI can also have adverse effects on physical health, as emphasized in the meta-analysis of Chou et al. (2016). This is primarily due to the sense of powerlessness it engenders, as discussed by Wallerstein (1992). Moreover, periods of economic uncertainty may lead to significant shocks in terms of mortality, as suggested by Bhattacharya et al. (2013), and have implications for labor-market outcomes, as explored by Gathmann et al. (2020). Recent research has delved into the connection between EI and various sociopolitical phenomena, such as support for populist parties (Guiso et al., 2020; Guriev and Papaioannou, 2022, among others), a lack of trust toward the EU (Algan et al., 2017; Dustmann et al., 2017; Foster and Frieden, 2017), the results of the 2016 US Presidential election (Inglehart and Norris, 2016; Mutz, 2018, among many others), and the 2016 UK referendum on EU membership (e.g., Sampson, 2017; Colantone and Stanig, 2018). In Bossert, Clark, D’Ambrosio and Lepinteur (2023) EI is shown to predict greater support for Donald Trump before the 2016 US Presidential election, the UK leaving the European Union in the 2016 Brexit referendum, and Conservative political parties in both the UK and Germany.

The second axis of the EICCA DTU will build on the literature that shows why EI matters, and provide new evidence on its effects on a number of critical unexplored areas. We aim to provide the first evidence of the effects of EI on migration choices towards countries that offer a more protective welfare state. Another open but related question is how, and if, EI affects the integration of immigrants (see also Gathmann and Garbers, 2023).

• One PhD position in Economics or Social Sciences on the consequences of EI for migration choices and the integration of migrants: investigate how EI influences migration flows, location choices and integration in host societies.

Little is known about the interplay between the various factors affected by EI. Despite the clear relation between EI and political outcomes, we know surprisingly little about how this relation exactly works. We aim to explore how the connection between EI and political outcomes is mediated by public health. People experiencing EI frequently postpone medical care, forgo screening tests, and develop anxiety and depression due to a loss of self-esteem, social isolation, and unhealthy coping behaviors. In turn, this increased health vulnerability generates frustration and disappointment with the current political status quo, which can result in an increased support for populist parties. We aim to look at how different types of public-health policies affect the relationship between EI and political outcomes, as well as to examine how political actors use issues related to public health and EI to mobilize voters and shape the political discourse.

• One PhD position in Political Science on the consequences of EI for health and political outcomes in the EU: leverage natural experiments and policy changes to identify how health and political outcomes are shaped by EI

Currently, to the best of our knowledge, there is no study on the consequences on EI due to automation, the digitalization of jobs, and the effect of artificial intelligence in general (see also the following discussion on the causes of EI in this framework). We aim to answer the following questions: Do workers take proactive measures to protect themselves against EI due to the risk of job loss, including decisions to seek insurance coverage and increase savings? Do they look for safer jobs, relocate to safer areas, join unions and ask for training? How do firms’ hiring and restructuring decisions respond to EI? Will EI impact the human-capital investments of the future workforce? What type of welfare state do we need with interrupted working careers? What should the current institutional setup of public unemployment insurance and public pension schemes look like in order to provide economic security?

• One PhD position in Economics on the effects of automation, the digitalization of jobs, and artificial intelligence on EI: theoretical, experimental and empirical approaches to understand how technology-driven EI affects our societies

Surprisingly, there are no papers, to the best of our knowledge, analysing the causes of EI in general. The third axis of the EICCA DTU fills this gap by looking at the effects on EI of the successive shocks such as the Great Recession, the refugee crisis, the Covid-19 Pandemic, and the conflict in Ukraine.

In the current context of rapidly changing price structures and lifestyles (particularly housing costs and home ownership), alongside long-term societal and demographic transformations, it is increasingly relevant to understand the potentially deepening divides between the remaining stable groups in the labor force and diverse, fragmented groups expericencing stronger EI. Precarization affects unskilled and also highly-educated individuals, it varies across the life course from young people to those close to retirement, and it impacts differently a wide diversity of household structures, ethnocultural backgrounds, and social generations (Chauvel and Schröder, 2014). Measuring and understanding the various effects on EI of these different groups, particularly when adverse shocks occur (loss of job, income, health, relatives, etc.), will help promote resilience in different socioeconomic groups.

EI also results from the long-run transformations of our societies and demographic trends. These include the de-standardization of the life course due to the expansion of higher education, which has diversified student bodies and altered traditional timelines for education and career development. Such changes impact the timing of parenthood and other adulthood transitions like entering the workforce and leaving the family home. Additionally, population aging, with increased life expectancy and declining birth rates, presents new social and economic challenges. Family structures are also transforming, with fewer and shorter marriages, rising cohabitation, and more single-parent and blended families. These shifts reflect a move towards more varied life trajectories, necessitating new policies to support these evolving patterns (see Macmillan, 2005, among others).

A recent study in Germany confirmed that around 22% of individuals aged 80+ live in poverty, with this number being even higher for women and those with lower education (Fey and Wagner, 2021). Consequently, fear of financial losses and poverty in later life are widespread (e.g., Kornadt and Rothermund, 2011), increasing EI in middle age. Numerous studies have shown that such negative views of the future and fear of aging have detrimental consequences for late-life preparation and de-motivate preparatory efforts (Kornadt et al., 2015; Rupprecht et al., 2021). Given that individual preparation and planning for later life might help to mitigate the risk of poverty and financial losses, and contribute to overall health and wellbeing in later life (e.g., Adams and Rau, 2011; Stawski et al., 2007), this is of particular importance. We aim to better understand the role of EI in middle-aged adults over and above financial status and financial literacy. In particular, we will explore group differences in these relations, with a focus on gender and education as two of the major factors related to poverty in later life.

We will also study the impacts of climate change on EI. For example, we aim to investigate the implications on EI of higher temperatures in urban areas, particularly due to the concentration of residential and industrial buildings. Coupled with pollution, elevated temperatures in urban environments have been linked to worsening health, particularly among vulnerable groups such as the elderly and young children. This deterioration in urban living conditions disproportionately affects impoverished areas with limited living space and inadequate investments in housing protection and amenities. Climate change may as a result lead individuals to move, producing migration both between and within urban and rural areas. Last, climate has been linked to social unrest. Research (see Blakeslee et al., 2021, Heilmann et al., 2021, among others) has revealed a correlation between higher temperatures and the likelihood of criminality and urban unrest, as observed in historical events such as the Zoot Suit riots in June 1943, the Watts riots in August 1965, the Ferguson riots in August 2014, and the George Floyd unrest in June 2020, all of which occurred during Summer periods. Crime and unrest will likely impact EI.

We will look at the effects on EI following the digitalisation of jobs, and artificial intelligence in general. Are certain groups more exposed than others to these risks? More and longer unemployment spells are likely to become more common. Structural shifts in the demand for labour will affect the distribution across the economy. But what sectors and what kind of jobs will be more affected? Will these effects be more pronounced in aging societies due to the jeopardization of jobs currently held by the older workforce?

We will here build on research on the causes of insecurity on the labour market, i.e. job insecurity, which is more abundant. This literature can be split into two main streams. The first examines the factors contributing to objective job insecurity, such as unemployment. There is an extensive body of literature that considers the role of institutions and policy reforms (Saint-Paul, 2002; Bassanini and Duval, 2009, Feldmann, 2009; Skedinger, 2011), as well as that of technology (Acemoglu and Autor, 2011, Acemoglu and Restrepo, 2018). While less extensive, the second branch of research centres on perceived job insecurity, which pertains to the fear of imminent job loss. Some analyses concentrate on macroeconomic factors (Clark and Postel-Vinay, 2009, Farber, 2010), and others on managerial and company practices (Feather and Rauter, 2004). In a recent article, Lepinteur (2023) compares the influence of having a public-sector job and a permanent contract on the perception of job insecurity in France and Switzerland. This study underscores how context-sensitive the effects of these variables are.

The fourth and last axis of the DTU covers policy actions. Our goal is to better understand the role of policy-making institutions in confronting the challenges posed by EI.

The first step will be to develop a theoretical model of policy making that is sufficiently flexible to accommodate various political institutions, policy uncertainty in the face of EI, and endogenous perceptions of EI. The second step will be to explore this framework’s implications for the role of political institutions in developing appropriate policy responses to EI, how these institutions can be better designed to address EI, and how they impact and are impacted by public perceptions of EI.

• One PhD position in Economics on public policy and EI: theoretical approaches

Experimental and applied evidence will shed light on the eventual finding of heterogeneity in individuals’ perceptions. In view of these channels, we aim to understand how to optimally design institutions to minimize the rise of EI. Alarming socioeconomic trends can induce perceptions of insecurity and various behavioural reactions.

The experimental project will investigate the gap between perceived and actual insecurity. To understand these gaps, we will develop theoretical and quantitative measures of pessimism, using both forward-looking (ex-ante) and reflective (ex-post) assessments. Online surveys will be conducted to distinguish between objective and subjective insecurity and its link with pessimism. Additionally, we will utilize randomized treatments triggering optimism or pessimism to assess their impact on short-term attitudes and behaviors. These behaviors include preventive and coping strategies (such as savings, investments in physical or human capital, and career changes) and short-term unproductive patterns (procrastination, excessive spending, allocation of time or resources to high-risk investments or safe but low-pay strategies, neglecting health,…). We will then study whether certain personality traits and behavioral biases (e.g., present bias, misinference, projection bias) shape these responses. Thanks to these randomized treatments, we will provide recommendations for policymakers to encourage optimal behaviors and mitigate economic insecurity.

Public policy can mitigate EI by providing social safety nets and social insurance mechanisms. However not all such programmes are equally effective in buffering economic shocks, and their effectiveness may depend on the institutional and economic contexts in which they are implemented. Furthermore, the provision of social insurance schemes might also influence the behaviour of individuals themselves, and this may in turn conflate or mitigate vulnerability to economic risks. There is therefore a need to examine and rigorously quantify the impact of different forms of social insurance mechanisms on behaviours and EI outcomes across different socio-economic environments. We aim to provide such analyses by exploiting various sources of micro-data (longitudinal, administrative or survey-based) to study the causal effect of public policy interventions on EI. We expect here to leverage newly-developed AI-based tools (e.g., to automate data collection (such as coding of policy documents), for predictive modeling (transfer learning) and/or for identifying heterogenous effects in causal inference).

Following the earlier contribution of Osberg and Sharpe (2009), we aim to provide an in-depth analysis of the impact on EI of the protection offered by the Welfare State in EU countries, and micro-simulate the effects on EI of various shocks to household economic resources.

The microsimulation project also aims to investigate the relationship between rising income inequality, EI, redistribution, and trust in government institutions focusing on historical changes across the three economic crises: the Financial, Covid-19 and the Cost of Living Crises. Trust in institutions plays a vital role in accomplishing shared objectives: it was essential during the pandemic, will be crucial for addressing climate change, and is necessary for fostering social solidarity. Recognizing that public trust is essential for greater compliance with a wide range of public policies, political participation, social cohesion, and institutional legitimacy, this project seeks to establish a connection between the drivers of changes in inequality, EI, redistribution and trust in institutions during these crises.

The research will focus on EU countries and make use of the EU Statistics on Income and Living Conditions (EU-SILC) and the European Social Survey (ESS). We will here build upon a microsimulation micro-econometric decomposition approach, extending the model developed by Sologon et al. (2021) and Sologon et al. (2022) to quantify the contribution of four drivers of EI: tax-benefit systems, employment and occupational structures, labour prices and market returns, and demographic composition.