Understanding high inequality and its dynamics is essential for public policy-making due to its adverse effects on societies from both economic and social perspectives. Inequality hampers not only economic growth but also causes important social unrest, as witnessed in North African countries such as Tunisia.

Welfare and inequality (defined as the uneven distribution of welfare) have mainly been measured in monetary terms. Although the necessity of including non-monetary dimensions to better understand these phenomena has been gaining ground  (viz. UNDP’s commonly used Human Development Index including health and education), the role of employment has not been sufficiently emphasized. Yet, employment constitutes a key component of welfare and inequality not only through income but also through other conditions such as type of contact or working hours. It also affects considerably the feeling of self-respect and fulfillment.

Therefore, this study develops a multidimensional measure of welfare that contains non-monetary dimensions linked to labor market conditions. Then, we analyze the distribution of this welfare using several inequality measures (Gini coefficient and the General Entropy family of indices). The method is applied to survey data in a selected number of European countries to evaluate the changes in inequalities before and after the 2008 global economic crisis.

Our welfare measure is constructed by combining various dimensions (e.g. income, health, employment, contract type…) into one index of welfare, instead of examining them separately. This approach has the advantage of taking into account the interrelations between different dimensions and allows complete ordering of distributions. It also gives a clear overview of changes over time. However, the aggregation procedure raises questions about the weight given to each dimension (their relative importance) and the substitutability level between them (if and at what rate a loss in one dimension can be compensated by a gain in another). These issues require normative assumptions that might strongly affect the outcomes. In this regard, it is essential to use various approaches to determine how robust the model is or what difference particular choices make. We use two different techniques to derive weights: the first one is the commonly used assignment of equal weights, and the second consists in regressing self-assessed satisfaction from life on a set of variables of interest. This is based on the idea that if the objective of welfare measures is to give an indication of how good the individual’s life is, self-assessed satisfaction might be reasonably seen as an indicator. Different values are also assigned to substitutability levels.

Policymakers face the challenge of making decisions with limited resources. By allowing the use of different parameters, our method can be a powerful tool for them to adjust their policies according to their priorities and analyze their impact. They can emphasize the role of certain dimensions, or exclude some of them, modify the relations between them, and determine the consequences of such choices. The method can thus assist the policy-makers in making decisions on how public resources should be divided between different policy objectives.