Spatial directional robust Benefit of the Doubt approach in presence of undesirable output: An application to Italian waste sector

Abstract

This paper introduces a new composite indicator method integrating the spatial dependence into the robust directional model in the case of undesirable outputs. The proposed approach is advantageous compared to the traditional and conditional robust BoD models in that it allows to compare the performance of individual units with local cluster of peers. The methodology has been tested on a very detailed database of Italian municipalities for the year 2015 in the municipal solid waste collection and processing sector and confirms the existence of strong local constraints linked to the disposal facilities planned by higher level Authorities.

Dynamic Inefficiency and Spatial Spillovers in Dutch Dairy Farming

“…This is achieved by allowing dynamic inefficiency to be a function of both individual and neighbour characteristics as Skevas and Grashuis (2020) do in a static inefficiency framework. Although not using exact location information of latitude and longitude and not applied to agriculture, Fusco et al (2018Fusco et al ( , 2019 present an alternative single-step DEA approach in which the inefficiency of each unit is measured relative to other units that belong to the same territory and where the number of neighbours is estimated empirically. However, this study uses the Skevas and Grashuis (2020) approach where inefficiency is first calculated based on a common frontier and spatial spillovers are sought in a second-stage regression.…”

Economic Analysis of Portuguese Public Hospitals Through the Construction of Quality, Efficiency, Access, and Financial Related Composite Indicators

“…We did not evaluate it to avoid a too large manuscript. However, we expect, in a little while, to compare the current results to others achieved using different aggregating methodologies, including the robust order- m BoD (Fusco et al 2019 ; Vidoli and Mazziotta 2013 ), the multiplicative BoD (Verbunt and Rogge 2018 ; Van Puyenbroeck and Rogge 2017 ), the Mazziotta-Pareto Index (De Muro et al 2011 ; Mazziotta and Pareto 2020 ), and the multicriteria decision analysis tools belonging to the ELECTRE or UTA families (Samira et al 2019 ).…”

Local government size and service level provision. Evidence from conditional non-parametric analysis

“…In the second case, the more the municipality restricts the sub-indicator the better it is (e.g., the level of crime). To encompass both desirable and undesirable sub-indicators, the model builds on a directional distance function as introduced by Zanella et al (2015, models 7 and 8 p.523) and recently used by other scholars (see for example Rogge et al, 2017;Fusco et al, 2019;Lavigne et al, 2019). As for the second issue, the deterministic nature of the basic BoD model makes each municipality in the sample constitute the performance possibility set.…”

“…To mitigate the influence of atypical observations and to include characteristics of the operational environment, the robust conditional directional distance version of the "Benefit of the Doubt" (BoD) model is considered, combining insights introduced by Cazals et al (2002); Daraio and Simar (2007b); De Witte and Kortelainen ( 2013), extended to the directional distance framework by Simar and Vanhems (2012); Daraio and Simar (2014) and adapted for the directional distance BoD composite indicator by Fusco et al (2019). With this model specification, a municipality's service provision is not evaluated against the service provision of all the other municipalities in the sample, but rather against municipalities with equal or better performance and with similar background conditions or, put another way, against the expectation of the "best" (Daraio et al, 2020).…”

“…Moreover, to acknowledge the broad range of functions of municipalities, we include assurance region type I weight restrictions that mimic the relative importance of municipal areas in percentage terms using information from the actual budget allocation. Thanks to the insights from robust conditional efficiency analysis (Daraio and Simar, 2005;Simar and Vanhems, 2012;De Witte and Kortelainen, 2013;Daraio and Simar, 2014;Fusco et al, 2019), we immediately control in the construction of the composite indicator for observed heterogeneity coming from contextual elements such as fiscal income, financial debt or unemployment. As such, the environmental context that municipalities are facing, can partly explain their choices in the service level provision.…”