Abstract
This paper examines the interaction of productive efficiency, water quality and organizational type. A water quality index is constructed employing various contaminants which are then treated together with variables including organizational type as endogenous in an input distance function model. The cost of drinking water quality and inefficiency are derived and hypothesis tests estimated concerning their variation by location, organization type and water quality. The key findings are that more technically efficient water utilities deliver higher quality drinking water and that private firms distribute higher quality water at a higher price and at a higher implicit cost than public utilities despite no overall inefficiency differences between the two organizational types.
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Notes
Nitrate results from fertilizer use runoff, and leaking from septic tanks and sewage as well as erosion of natural deposits. Infants below 6 months of age who drink water containing nitrate concentrations above the MCL could face serious illness, such as the so-called blue baby syndrome (very rare), and if untreated could die. Turbidity measures water cloudiness, and it results mainly from soil runoff. A high level of turbidity points to micro-organisms that can cause stomach problems and headaches. Chlorine employed to control microbes in drinking water, can cause eyes and nose irritation and stomach discomfort. Long-term exposure to total trihalomethanes (TTHMs), a drinking water disinfection byproduct, above MCL results in a potential increase in cancer risk, as well as in kidney, liver and central nervous system problems (EPA 2013).
This short run cost function corresponds to that employed by Morrison (1999) to characterize short run and long run dynamics.
This follows Coelli et al. (2008).
See Christensen et al. (1973).
Destandau and Garcia S (2014) have a forthcoming article also uses 1996 AWWA data.
The author consulted energy sources employed by CWSs with AWWA engineers.
Refer to http://www.eia.gov/electricity/data/state/ (EIA-861).
This will produce a bias measure of capital if a firm is not earning a zero or "normal profit" and/or if it is not using capital in a cost minimizing ratio with other inputs. This bias could be significant given the large number of public firms.
For the primary, enforceable, and secondary list, offered as guidance, of contaminants that EPA regulates or recommends see http://water.epa.gov/drink/contaminants/index.cfm#List.
The limitation of this approach is that the firm, as noted by Morrison (1999), does “revalue its capital stock.” In addition, consistency with the distance function assumptions, would involved an observed price of capital and a shadow quantity.
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I thank the editor and four referees for their advice with this article. In addition, I would like to thank Knox Lovell, Rolf Färe and Robert Russell for their feedback, and Laura Moore for her editorial and other help. All remaining mistakes are my responsibility.
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Appendix
Appendix
Following Caves et al. (1981) and Atkinson and Primont (2002), scale economies for a short run distance function is derived by assuming the existence of the short run transformation function:
where Y i are outputs; X i , inputs; Z i , quasifixed inputs and t represents time. The total differential is:
For dt = 0, it follows that:
Applying rule III of differentials in (Chiang 1984):
This expression for scale economies following Caves et al. (1981, pp. 995–996) is defined as the proportional increase in all outputs resulting from the proportional increase in all variable inputs holding time fixed and conditional on the levels of the quasifixed inputs.
Using the duality of the variable cost function, \( VC = \sum\nolimits_{i = 1}^{n - q} {W_{i} X_{i} } , \) with (44) by substituting Eqs. (8a), (8b) and (9) into equation (11) of Caves et al. (1981) in p. 996, Eq. (44) can also be derived [see also the scale elasticity expression, Eqs. (8a), (8b), where the Z variable represents fixed capital, in Garcia and Thomas (2001, p. 15):
In terms of distance functions (Atkinson and Primont 2002) Eq. (45) becomes:
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Mosheim, R. Under pressure: community water systems in the United States—a production model with water quality and organization type effects. J Prod Anal 42, 277–292 (2014). https://doi.org/10.1007/s11123-014-0406-y
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DOI: https://doi.org/10.1007/s11123-014-0406-y