There are two theoretical approaches to estimating efficient costs: top-down and bottom-up. Top-down models evaluate systems using aggregate economic variables, whereas bottom-up models consider technological and contextual characteristics that vary across service providers.<sup>7</sup> Due to the requirement for detailed data on specific assets or activities, bottom-up models have thus far been restricted to local and regional estimations. For example, Ebinger (2004) uses a Hidden Costs Calculator model to estimate total subsidies in the energy and water sectors for a subset of countries within Europe and Central Asia. Countries are limited to those with sufficient data on costs of operation and asset values in order to calculate an average cost recovery price, from which actual revenue is then subtracted. Meanwhile, Clark et al. (2002) develop equations to estimate the cost of system construction, expansion, and rehabilitation and repair for water distribution systems in the United States.<sup>8</sup> Similarly, Marchionni et al. (2015) propose cost functions for various water supply system assets in Portugal on the basis of known hydraulic and physical characteristics.

Most commonly, bottom-up models are used at the country level for the regulation of various types of service provision. In telecommunications, the hybrid cost proxy model (HCPM), a forward-looking economic cost model developed by the United States’ Federal Communications Commission, is the best example of a pure bottom-up model used for regulatory purposes (FCC 2009, Gasmi et al. 2002). In the electricity and WSS sectors, the model firm ( _empresa modelo_ ) approach has been applied in several Latin American countries, notably in Chile (Chavez 2002, Dammert et al. 2008).

Chile adopted a model firm approach to WSS regulation in 1988 through Decree DFL no. 70/1988, which seeks to induce efficiency by setting tariffs based on efficient operation and maintenance costs, while accounting for necessary capital expenditures to meet expected future demand. As described by Bitrán and Arellano (2005), “In Chile, to avoid transferring the cost of inefficiencies to users, the rate setting process emulates competitive conditions by using a fictitious company that would theoretically meet demand over the next five years in the most efficient way.” The paucity of data on the costs of WSS service provision in most countries makes estimating subsidy levels for networked services at the global level a daunting task. Due to these data constraints, prior global estimations of subsidies have taken a top-down approach. For example, Kochar et al. (2015) estimated global WSS subsidies using a price gap approach by subtracting actual revenues from estimated costs. The authors use a reference full cost-recovery price of $1 per cubic meter, which is taken from work done by the Global Water Intelligence in 2004 (GWI 2004) and is assumed to be the same for drinking water and wastewater. They then adjust this price for each country to account for three factors: (i) general price inflation that occurred between 2004 and 2012; (ii) lower labor costs in low- and middle-income countries; and (iii) varying levels of water scarcity.

> _7_ As noted by the Intergovernmental Panel on Climate Change: “The basic difference is that each approach represents technology in a fundamentally different way. The bottom-up models capture technology in the engineering sense: a given technique related to energy consumption or supply, with a given technical performance and cost. In contrast, the technology term in top-down models, whatever the disaggregation, is represented by the shares of the purchase of a given input in intermediary consumption, in the production function, and in labor, capital, and other inputs. These shares constitute the basic ingredients of the economic description of a technology in which, depending on the choice of production function, the share elasticities represent the degree of substitutability among inputs.”

> _8_ Rather than calculating an aggregated cost function the authors estimate different cost functions for unit operations such as base installed pipe; trenching and excavation; embedment; backfill and compaction; valves, fittings, and hydrants; dewatering; and so on.

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