Marije Kooistra (p), Richard Rijnks, Aleid Brouwer

Eveline van Leeuwen

Beyond-GDP frameworks are gaining traction as policymakers increasingly prioritize well-being and quality of life over material wealth. While research has explored the relationship between well-being domains and life satisfaction, their relative importance is often unknown (Clark, 2018; Samavati & Veenhoven, 2024). This study addresses this gap by assessing how different well-being domains contribute to life satisfaction across the distribution. Using the broad prosperity framework commonly used in Dutch policymaking, we analyze the relative importance of seven domains (CBS, 2023). To account for variation across the distribution, we employ quantile regression and stratify by age groups. Additionally, we make a methodological contribution by applying Gradient Boosting, a machine learning approach which handles non-linearity and multicollinearity well and was shown to be promising for wellbeing research (Oparina et al., 2025).

Using 2024 survey data (n=5409) from two Dutch provinces, we found that some domains are indeed more important than others. There is fairly limited variation in the order of domain importance across age groups and across the distribution of life satisfaction and that GBQR (Gradient Boosting Quantile Regression) has both advantages and disadvantages compared to linear regression in explaining life satisfaction.

Our results indicate that some domains are consistently more important than others, with health—particularly mental health—emerging as the most significant factor. This highlights the necessity of accessible mental health services. Material wealth ranks fifth in importance, following labor and leisure (which include satisfaction with work, daily activities and free time), social cohesion, and housing. The remaining domains each explain less than 5% of the variance in life satisfaction. Domain importance is consistent across quantiles, meaning that in general what is important for those at the lower end of the distribution is no difference in importance from those at the top end. Overall, the GBQR model preforms well with the pseudo R-squared exceeding 0.5 at some quantiles. At the extreme quantiles it is, however, outperformed by linear regression. The models for individuals under 35 and those aged 35–55 exhibit the highest explained variance.

Our main contribution is the improved understanding of the dynamics of wellbeing frameworks. While it provides empirical backing for domain weighting in wellbeing monitoring frameworks, it also provides arguments against this practice.  Variations in relative importance suggest that weighting domains based on mean importance may misrepresent certain groups, particularly younger individuals.

M. Carmen Lima (p), Ferran Sancho

Eveline van Leeuwen

This paper focuses on a contribution to improve multisectoral national/regional analysis. The tools currently used to identify and measure forward effects in disaggregated multi-sector models are widely considered inadequate. While the multi-sector approach is broadly accepted, the Ghosh classical model (1958), commonly employed in these analyses, has faced substantial criticism for two primary reasons. First, the model fails to accurately represent the technological characteristics of a market economy. Second, it gives rise to conceptual inconsistencies in the relationship between value-added and output levels. To overcome these limitations, we propose a new and straightforward method, grounded in standard theory, for identifying and measuring forward effects. Although this approach departs from the Ghosh model, it retains the core principle of examining the dependence of output on value-added, while enabling wider insights for economic analysis and policy evaluation.

Eveline van Leeuwen (p), Aleid Brouwer

Marije Kooistra

Economic growth has been a dominant goal and narrative, basically since the development of GDP after the Second World War. The modern concept of Gross Domestic Product (GDP) was first developed by Simon Kuznets for a 1934 U.S. Congress report, where he warned against its use as a measure of welfare. After the Bretton Woods Conference in 1944, GDP became the main tool for measuring a country's economy. And it not only became the main measuring tool, economic growth (as measured by GDP) also became the ‘main solution for everything’: from poverty, unemployment, environmental destruction, climate change, financial instability, etc. (Daly, 2019). Many scientists, including Noble prize winner Joseph Stiglitz, have shown that society in general and the economics discipline more specifically needs a different compass than GDP (Hoekstra, 2019). Alternative economic models have been proposed, such as Doughnut Economics, Sustainable Development Goals, Steady State Economics, De-growth and Green Growth (D’Alessandro et al., 2020). The fundamental value which these models have in common is that they make a distinction between present wellbeing, future wellbeing (sustainability) and also the distribution of wellbeing within and between countries (inclusion). But how to conceptualize and measure this in a way that is meaningful (i.e. measuring what matters) and clear (through a moderate number of indicators), as well as appropriate for the right spatial scale?

In this paper, we analyze how economic growth is used within the regional science literature: to which topics is it linked, what conclusions are drawn and how is this translated into policy recommendations? In a next step, we will analyze to what extent new concepts as broad prosperity and postgrowth are used and implemented.
To answer these questions, a structured literature review within the ERSA/RSAI journals will be performed.