Operational Efficiency of Mexican Water Utilities: Results of a Double-Bootstrap Data Envelopment Analysis

Abstract

The objective of this paper is to estimate the operational efficiency of Mexican water utilities and identify the context variables that impact their efficiency. In particular, a bootstrap data envelopment analysis (DEA) and a bootstrap truncated regression analysis are combined in a two-stage research method. In the first stage, an input-oriented DEA model is used to determine bootstrap efficiency scores. Then, the corrected distribution function of the efficiency scores is used to estimate a truncated regression which is aimed to identify the significant influential context variables. Three categorical and two continuous context variables are considered in the analysis. Results show that only one context variable has a significant impact on the water utilities efficiency scores. Managerial recommendations are drawn from the analysis. It is suggested that water utilities continue or implement wastewater treatment, persist in decreasing and controlling leakage across the distribution network, and maximizing sewer coverage.

Evaluating the Efficiency of Water Distribution Network Sectors Using the DEA-Weight Russell Directional Distance Model: The Case of the City of Valencia (Spain)

“…Recent studies have used complex variants of the DEA, such as super-efficiency (e.g., to investigate the potential for efficiency improvement [4]), DEA-scale efficiency (e.g., to examine the efficiency of leakage-management [14]); DEA model with statistical tolerance (e.g., to assess the efficiency of water and sewerage companies (WaSCs) [38]); shared input data envelopment analysis model (e.g., to separately measure the efficiency when the same operator delivers more than one service, as water and wastewater services [39]); the directional metadistance function (e.g., to revisit the relationship between ownership and performance [13]; Bootstrap DEA (e.g., to identify the determinants of efficiency of water provision services [40,41] or to evaluate the influence of the management nature (private vs. public) on efficiency [10]); DEA double bootstrap (e.g., to overcome the limitation of deterministic method that does not allow identifying environmental factors influencing efficiency scores [42]); the DEA-based approach on Directional Distance Function (DDF) (e.g., to measure the performance of the integrated production of desirable and undesirable outputs [43]); or Network DEA models (e.g., to overcome the shortcomings of the standard AED for relative performance assessment (which do not allow setting clear guidelines for improvement) [44]).…”

“…In the literature reviewed, a commonly used input variable is operation or maintenance costs [2][3][4][5][6]8,13,[23][24][25]37,42], but other costs have also been considered, such as staff cost [3,4], indirect labor cost for pipe network management [14], capital expenditure [37], amortization and taxes, water purchased and energy cost for producing drinking water [3], total expenses [71], or total production cost [10]. In some works, the production inputs have been included, such as labor in absolute terms [6,8,13,24,42], relative terms (staff per 1000 connections [5], number of employees per 1000 consumers [41]), total assets or capital stock [2,23,25], and energy [4].…”

“…Network characteristics are used in many studies as an input variable, including the length of the network [2,4,[6][7][8]10,13,26,42], water distribution [41], number of water pump houses, wastewater pump houses [2], sewerage network length [10], and leakage (directly or indirectly [26], such as through non-revenue water [5]). It should also be noted that costs related to water leakage have been included, such as active leakage management cost, rapid, accurate leakage repair cost, and appropriate pipe body management cost [14].…”

“…Its importance lies in the fact that it represents the best technical condition of the water supply network, has implications for the quality of the service (quality of water and quantity of water supplied), and is even often used as an indicator to assess the degree of development of a country [74]. It is one of the most frequently used desired outputs in works on the efficiency of water supply services under different specifications: efficiency in reducing leaks [14], indicators such as the index of the reciprocal of water [2], or the ratio of water volume paid to water volume produced [41]. In addition, some studies [37,42] have also included the quality of the water supplied.…”

Effect of Environmental Fairness on Assets Utilization in the Nigerian Oil and Gas Companies: An Empirical Analysis

“…Measuring operational efficiency in an optimum way depends on the industry involved and the quality of data available. For example, in the water industry, Ablanedo-Rosas, Campanur, Olivares-Benitez and Nunez [1] suggest the use of bootstrap data envelopment analysis (DEA) and bootstrap truncated regression analysis methods. Similarly, for waste management, Dias, Madaleno, Robaina and Meireles [11] recommended the use of the collection capacity use (CCU) and the segregated waste collection efficiency (SWE) methods.…”