Alberto Díaz-Dapena (p), Ana Viñuela Esteban, Fernández-Vázquez, Fernando Rubiera-Morollón, Elena Lasarte-Navamuel

Ana Patricia Fanjul Alemany

Drivers of inequality and poverty in Europe are hardly studied at a local. Lack of disaggregated data makes almost impossible to obtain accurate proofs about these processes. However, differences between rural and urban areas are creating an enormous gap within countries. Populations in these areas seem to feel ‘left behind’ from the rest of society, with no future prospects or public policies for them. ‘Left behind’ places are becoming more important in the Regional Economics literature as populist processes gain influence in these areas and drive a new age of policies. As a result, this research tries to obtain reliable indicators to measure the economic and social evolution of these areas.
This research applies a Small Area Estimation process based on Tarozzi and Deaton (2009) and Fernández et al. (2020, 2021). The main idea of Tarozzi and Deaton (2009) is using microdata from a household survey (in our case EU-SILC) with accurate information about the variable of interest and microdata from census, with precise information about location of households. If both databases share a common set of variables it is possible to extrapolate the value of the variable of interest over the households in the census. Then, these estimates can be used to obtain new aggregates at a lower scale.
Fernández et al. (2020, 2021) updates this procedure with an entropy econometrics approach to make them consistent with official aggregates. As a result, it is possible to correct a set of prior estimates, or the researcher can directly obtain a set of parameters given a restriction of the national aggregates. Through these estimates, this research expect to identify the depressed areas of the European union, as well as their evolution over time in the period 2011 – 2021.

Alfredo Cartone (p), Luca Di Battista, Paolo Postiglione

Alberto Díaz-Dapena

The reduction of inequality represents a critical issue for the European Union. In this context, the modelling of poverty convergence more recently emerged to test the potential reduction of inequalities. In this paper, we aim at a deeper understanding of poverty convergence at regional level. First, we extend the concept of absolute poverty convergence to conditional beta-convergence at regional level and account for poverty measured by AROPE, a key indicator for policy makers in the EU. Second, we use the statistical methodology of quantile regression to provide a broader assessment of the heterogeneous relationships between poverty and economic factors. Lastly, we draw on the spatial econometric literature and use a spatial quantile regression methodology that considers geographical interconnections at regional level. Moreover, in the study regions are classified using a novel indicator built as the distance from the efficient quantile (in our case the lowers, in terms of conditional poverty reduction) to express the ability of a region to tackle poverty. The indicator is used to classify regions and highlight the presence of within country differences. The results confirm the importance of using spatial models for considering potential spatial autocorrelation in poverty convergence. Also, they suggest how working with neighboring regions is relevant to coordinate efforts to tackle poverty.

Ana Patricia Fanjul Alemany (p), Liliana Herrera, María F. Muñoz-Doyague

Alfredo Cartone

There is no doubt that territorial inequalities in Europe are considerable. A paramount example are left-behind areas. Numerous policies have been developed for these regions, given the challenges faced by entrepreneurs in this territories and their huge potential for employment creation. One of such policies is Community-Led Local Development, which employs an innovative bottom-up approach. To analyze the latest wave of aid of this policy, a novel dataset has been created with over 12.6 million beneficiary projects over six years. This information is then aggregated at a municipality (LAU-2) level. To perform the analysis, given that we have a setting with multiple time periods and covariates, we employ the novel Difference-in-Difference estimator developed by Callaway and Sant’Anna (2021). The results show that the policy is indeed effective creating local employment, but the results are unequal between male and female workers. Finally, a spillover analysis is performed to examine whether the policy has effects that stem beyond municipality borders. In this case, no spillover effects are found.