A Micro‐Econometric Analysis of the Antipoverty Effect of Social Cash Transfers in Italy

Abstract

We analyze the anti‐poverty effect of social cash transfers using a micro‐econometric approach. Aggregate analyses, based on comparing average poverty indicators before and after public transfers, fail to address who receives the transfers and how the transfers are distributed among the poor. We consider three dichotomous outcome variables: (i) poverty status before the receipt of transfers; (ii) the receipt of transfers; and (iii) poverty status after the receipt of transfers. We use a trivariate probit model with sample selection, connecting the outcome variables to the characteristics of the household and its head. Our empirical results highlight that the Italian social transfers system overprotects certain household typologies at the expense of others, as social transfers are primarily awarded to employees with permanent positions and the elderly, while the system is not generous enough to large households with dependant children, the self‐employed, temporary contract workers, and the unemployed.

Assessing poverty persistence in households with children

“…We find that the number of dependent children increases both the risk of ARP (2.7 pp) and SP (1.5 pp), while it does not significantly affect SMD. The significant effect of the number of dependent children on the measures depending on current income is expected if transfers do not correct the reduction of the equivalised income caused by dependent children; the significance can then be explained in terms of the weak policies directed at backing families with children in Italy both at the fiscal and transfer levels (see Fabrizi et al 2014). The significant effect on ARP can also be related to the dependence of this measure on the modified OECD equivalence in the definition of the equivalised income; there is evidence in the literature of a possible underestimation of economies of scale when adopting this scale (Bishop et al 2014).…”

“…This finding also confirm that having a job (either high-skilled or low-skilled one), as explained above, is not enough to overcome the risk of poverty (Eurostat 2018). Possible alternative explanation, such as the effect of unemployment benefits being more generous than low wages are not very likely for Italy (see Fabrizi et al 2014 for a discussion and more references on this point.…”

“…1 Italy is experiencing a persistent very low fertility rate that, despite immigration from many different countries is causing a rapid aging of the population (Billari 2008). Moreover, although before the economic downturn a debate started on the opportunity to reform the social welfare and tax-transfer regime to sustain people in poverty conditions (Aaberge et al 2004), the current system of social transfers is still not well targeted neither on the poor nor on households with children (Fabrizi et al 2014). A closer understanding of poverty and social exclusion among households with children can provide useful insights for the country's future social policies.…”

On Italian Households’ Economic Inadequacy Using Quali-Quantitative Measures

“…Specifically, we observe some overlap in the distribution of poverty risks, with higher values in southern Italy than in northern Italy. The southern areas are characterised by the highest unemployment risk, the lowest gross domestic product (GDP) per capita, the lowest ratio of enterprises per inhabitant, the lowest municipal expenditures on social assistance, and the lowest allotment of public services for children (e.g., the nursery schools that represent an important service in the implementation of policies aimed at the reconciliation of home and work commitments), to name a few (e.g., D'Antonio and Scarlato 2008; Cracolici et al 2009;Fabrizi et al 2014;. These factors also explain why foreign citizens who enter in the country choose to locate their residence in northern Italy rather than in southern Italy (see Marra et al 2012).…”

“…In this regard, studies at the microeconomic level relative to Italy include the region of residence of individuals/households to capture the dichotomy between the north and south of the country (e.g., Anderloni et al 2012;Fabrizi et al 2014;Celidoni 2015). The standard of living of households will likely be affected by labour market characteristics, cultural aspects, and spatial welfare policies, to name a few.…”