Panel G

Data Analysis and Models

Enrico Nichelatti: A Fiscal Approach to the Social Contract in Sub-Saharan African Countries

The COVID-19 pandemic showed that many developing countries were unable to respond effectively to crises due to their limited capacity to diversify their social protection responses. Social protection systems depend largely on government tax revenue capacity. Raising more domestic revenue still represents a priority for most Sub-Saharan African countries that continue to face high rates of tax non-compliance. This research investigates whether there is a link between citizens’ perceptions of governance and individual tax compliance in SSA. We employ a logistic regression model and use round 7 of the Afrobarometer that contains information on Africans’ views on democracy, governance, economic reform, civil society, and quality of life for 32 countries (Benin, Botswana, Burkina Faso, Cabo Verde, Cameroon, Côte d’Ivoire, Eswatini, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritius, Mozambique, Namibia, Niger, Nigeria, São Tomé and Príncipe, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Uganda, Zambia, Zimbabwe). The main results suggest that perception of governance affects tax compliance, and its impact differs by country. Furthermore, the study proposes a binary mediation analysis to investigate the direct and indirect effects of governance perception on individual tax compliance, with trust in institutions serving as a mediator.

Henri Wiman, Carmen Antuña-Rozado & Markos Xenakis: Dynamics Over Precision: Simulating Post-Growth Institutional Transitions 

Sustainability transitions as a field of research struggles with articulating its theories of change in a precise way. Numeric modelling methods can help test the implications of ideas consistently and coherently. Transitions modelling has tended to use this potential for processing large amounts of variables at once. However, we argue that there are considerable advantages to be gained when de-emphasizing the detail and variety of variables and parameters (‘complicatedness’) and instead emphasizing the nonlinearity ingrained in theories of change (‘dynamic complexity’). Furthermore, there have been only a few transitions modelling applications based on socioeconomic critiques. These include post-growth economics, which argues that sustainability implies institutional changes that reduce the drive for and dependency on economic growth.

We start from a minimal dynamic model, namely the generalized exponential growth model (also known as the “Richard’s growth model”), that has illuminated understanding of growth dynamics in a large variety of natural science applications. For a selected sustainability transition question, we interpret drivers, barriers, and contingencies of change in terms of the model. Crucially, we add co-dependencies between variables and give variables alternative formulations to increase the dynamic options of the model without adding complicatedness. This allows us to test the implications of broadly articulate theories of change.  We show how the approach works for institutional change, particularly toward not-for-profit enterprise models that are thought to characterize a post-growth economy. We argue in favor of minimal dynamic models in scrutinizing and developing narrative theories of transition.

Xu Zong: Identifying Predictors of Mental Health of the Elderly During COVID-19: A Machine Learning Approach 

Scientific research about the mental health impact of the COVID-19 pandemic is still few. Prior research suggests that demographic factors, socioeconomic factors and other factors, are associated with mental health of the elderly during the Covid-19 pandemic. Less is known about what factors have the most important impact on the mental health of the elderly during the pandemic. The study aimed to identify predictors of mental health of the elderly during the COVID-19 pandemic. Participants from 28 European countries and Israel provides the information of various factors in 2021 that may affect mental health of the elderly. Machine learning algorithms were used to identify the strongest predictors of mental health of the elderly. The study findings reveal the relative importance of different factors for mental health of the elderly during the COVID-19 pandemic. And it will be beneficial to make evidence-based public health policy to improve the mental health of the elderly during the Covid-19 pandemic.

Keywords: Covid-19, mental health, machine learning