Ending child poverty in Spain: Calibrating the fiscal policy tools

Spain has the second-highest child poverty rate in the European Union (EU). Moreover, child poverty has been increasing since 2018, despite a context of economic growth and a decreasing overall poverty rate. The At-risk-of-poverty-or-social-exclusion (AROPE) rate for individuals under 18 was 34.6% according to the latest official statistics, substantially higher than the (also high) overall population rate of 25.8%.

From both an economic and ethical standpoint, reducing child poverty yields high returns. Drawing on Heckmans work on early childhood investment, resources directed at children during developmental periods generate the highest long-term returns by preventing future skill gaps and reducing social costs. From a social investment perspective, child benefits represent strategic investments in future workforce productivity rather than mere expenditure. Furthermore, Roemers theory of equality of opportunity underscores that children bear no responsibility for their households economic circumstances, and therefore child poverty would always violate fundamental fairness.

This research addresses a key policy question: What fiscal policies could most efficiently reduce child poverty in Spain, and what are the trade-offs between targeting and universalism? We employ EUROMOD, the EU tax-benefit microsimulation model, which simulates the effects of policy changes on household incomes using representative microdata from the EU Statistics on Income and Living Conditions (EU-SILC). We compare current policy rules against ambitious reforms targeted at reducing (child) poverty, including a universal child benefit and improvements to Spains national minimum income.

Our methodological contribution lies in systematically exploring the policy space through thousands of counterfactual scenarios, varying benefit levels and take-up ratios across different policy instruments. This comprehensive approach, enabled by running EUROMOD via an Application Programming Interface (API), allows us to map the efficiency frontier of poverty reduction, therefore identifying optimal policy combinations. Unlike conventional studies that compare a handful of discrete policy options, our method estimates the functional relationship between spending and poverty outcomes, providing policymakers with a complete menu of alternatives.

Results suggest that the current policy rules are markedly inefficient, as even a universal child benefit (a relatively expensive tool) shows a 33% higher cost-effectiveness. We discuss the causes for this, and suggest specific policy reforms capable of improving the tax-benefit system both in terms of budgetary cost and societal outcomes.