nights, Dr. Kim Rollins for the Industrial Economics Inc. adventure, Gaylene Nevers for ... Contents. 1 Virtual Water Content of Alfalfa Production in Northern Nevada . ...... _Water_Economics/Christopher_Lant/Virtual-Water-US.pdf ...... Idaho-Washington CGE Model," School of Economic Sciences, Pullman, WA: Washington.
University of Nevada, Reno
Water Use, Virtual Water and Water Footprints: Economic Modeling and Policy Analyses.
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Resource Economics By Elizabeth Fadali Dr. Maureen Kilkenny/Dissertation Advisor December 2013
UMI Number: 3608707
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THE GRADUATE SCHOOL
We recommend that the dissertation prepared under our supervision by ELIZABETH FADALI entitled Water Use, Virtual Water And Water Footprints: Economic Modeling And Policy Analyses be accepted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY
Dr. Maureen Kilkenny, Advisor Dr. Thomas Harris, Committee Member Dr. Klaus Moeltner, Committee Member Dr. Tigran Melkonyan, Committee Member Dr. Thomas Quint, Graduate School Representative Marsha H. Read, Ph. D., Dean, Graduate School December, 2013
i
Abstract The theme that binds together the four papers in this dissertation is the tracking of physical quantities of water used by industries in the economy, and an exploration of whether and how this tracking could be helpful in informing water policies, as applied to the state of Nevada or sub‐regions of Nevada. The concept of water footprints has been wildly popular in disciplines outside of economics and has been used to help make policy decisions normally considered to lie within the economist’s realm. Yet many economists shun ‘footprints’ in general and water footprints in particular, seeing them as descriptive methods that have little or nothing to add to policy analysis. This thesis attempts to bridge a gap between economists, engineers and planners and the popular imagination about what economic concepts footprints are related to and how they can best be used in policy analysis. In the first study I explore how much water is embodied in Northern Nevada’s alfalfa hay. In addition I estimate how much of this water is exported outside of the state. The essay uses the concept of virtual water as applied to northern Nevada hay, using the methodology espoused by the Water Footprint Network. The study serves to introduce the water footprint concept as well as to quantify the approximate magnitudes of a large part of Nevada’s virtual water trade. Alfalfa hay is Nevada’s most important crop export. Given how dear water is in the arid state for all types of uses, it is of interest how much ‘virtual water’ is being trucked outside of the state in the form of
ii hay. Also, two interesting methodological issues concerning economic policy analysis emerge from the study. First, virtual water usually measures water consumption rather than withdrawals, yet purchases of water in the region relate to water withdrawals and thus the more natural concept to include in an economic model. Second, the quantity of virtual water content in physical units such as acre‐feet said to be embodied in a product may depend on the relative value of joint products. Virtual water flows and a Nevada water footprint found through a different input‐output approach are the subject in the following essay. This essay begins a deeper exploration of how the descriptive virtual water flows and water footprints can be used for water policy analysis by including the use of a water computable general equilibrium (CGE) model. An application of the input‐output framework to Nevada shows that a relatively large proportion of agricultural products are exported to other states or countries along with a large proportion of the regions’ virtual water. The framework also shows, however, that Nevada is a net importer of virtual water. Nevada trade does not contradict the Heckscher‐Ohlin trade theory. Nevada exports a relatively more water thrifty mix of products and imports a relatively more water intensive mix. Two policy explorations suggested by Dietzenbacher and Velazquez in an earlier paper (2007) are carried out with the input‐output framework, then repeated in a computable general equilibrium (CGE) model and results are compared. In addition, the water resource and economic impacts of a ‘local food’ strategy are explored. The CGE model is found to be necessary for modeling water policy and in the two analyses performed, explicit notions
iii of virtual water are not needed since changes in direct water use can be measured directly. Having broached the subject of the water CGE model, a literature review of water CGE models as applied to finding the value of water follows. The third essay started out as a paper written for a grant addressing the question of how water‐CGE models can be used to find the value of water in industry to the economy and has been modified for the purposes of this dissertation. I define a ‘water‐CGE’ model as one that normally includes the tracking of physical quantities of water along with money transactions. The literature review finds that water‐CGE models for agriculture are well‐ developed, but that few exist that model water use in other types of industries. The types of policy analyses best suited to water CGE models are discussed. The ideas in this literature review are then applied and used to find southern Nevada values for water in agriculture, the water utility sector and other industry sectors. Comparing two scenarios, one allowing a market between industry sectors and one without a market, suggests a value for the Southern Nevada Water Authority pipeline water. One method for incorporating water as a factor of production into a CGE model is clarified and explained in detail, and a sensitivity analysis of initial rental values for the water factor indicates the how important these initial values may be. Finally, the last essay uses the ideas of virtual water flow with a state level CGE model to examine policies meant to encourage or discourage local food production.
iv Water footprints and the companion concept of virtual water have been used to suggest policy advice about trade in water intensive goods. For arid regions, the concept of virtual water has come to be associated with a political strategy of encouraging imports of food crops, especially grain, to overcome local water deficits. Amongst sustainability advocates, water footprints have been used as an analogue to eco‐footprints and carbon footprints often with implied policy advice to reduce these footprints by reducing imports of agricultural products in order to obtain resource self‐sufficiency. I test these policy recommendations under conditions that simulate global warming using a computable general equilibrium model. Discouraging the import of water intensive commodities is found to be harmful to economic outcomes yet is successful in reducing water resource use. In this analysis, the water footprint and virtual water concepts are useful in measuring how different policies may affect sustainability of resource use.
v
Dedication I dedicate this dissertation to the many friends and family members who received short schrift from me while I was under its sway. I hope you can forgive me and I thank you for your patience and support.
vi
Acknowledgements Many thanks to my advisor, Dr. Maureen Kilkenny, who spent many hours mentoring me, above and beyond the call of duty. Neither of us knew when she started out as my advisor that the department we both worked in would no longer exist before I finished, yet Dr. Kilkenny never allowed this to become a barrier. Her professionalism and her dedication to me as a student are much appreciated. My work has benefited from her knowledge, creativity and honest feedback. Special thanks also go to my committee member and long‐time employer, Dr. Tom Harris. Over the years he has provided me countless valuable opportunities to learn new skills and practice them. It has been a pleasure working for him and I would not be where I am today without his efforts on my behalf. Thank you to my committee members as well; to Dr. Tigran Melkonyan and Dr. Klaus Moeltner, who stayed with me as committee members long distance. Their departure from Nevada left the state’s human infrastructure in economics much diminished. A special thank you to Dr. Tom Quint. I will very much miss lunches at the Down Under with him and the gang. He always kept me laughing. I also would like to thank my co‐worker Malieka Landis for her constant support and faith in me, Dr. Anita Castledine for her “Dam‐It‐All” pills, Maggie Cowee for food bank nights, Dr. Kim Rollins for the Industrial Economics Inc. adventure, Gaylene Nevers for inspirational breakfasts, Corey Lott for the grant application suggestion, Marie Dennis for lunches, conversation and help with details, Genie Montblanc, Tabor Griswold, Dr. John Packham, Dick Bartholet, Mike Helmar and the many other students, staff and faculty who’ve been a part of my work and study family. Thank you for all your help and support. It has been a great pleasure to get to know you. I will miss you very much.
vii
Contents 1
Virtual Water Content of Alfalfa Production in Northern Nevada .............................. 1 1.1
Introduction .......................................................................................................... 1
1.2
Virtual Water and Water Footprints .................................................................... 2
1.3
Methods ............................................................................................................... 5
1.4
Results ................................................................................................................ 11
1.5
Conclusion .......................................................................................................... 13
1.6
References .......................................................................................................... 16
2 Dietzenbacher and Velazquez 2007 Revisited: Analyzing Nevada Virtual Water Trade with Input‐Output and CGE Models ................................................................................. 19 2.1
Introduction ........................................................................................................ 19
2.2
Geography and Water Use in Nevada ................................................................ 23
2.3
Description of I‐O Framework, CGE Model and Data ........................................ 25
2.3.1
IO framework ......................................................................................................... 25
2.3.2
CGE Model ............................................................................................................. 28
2.4
Direct and Indirect Water Use by Sector ........................................................... 30
2.5
Results ................................................................................................................ 32
2.5.1
Imposition of a Water Tax ...................................................................................... 32
2.5.2
Leontief Experiment ............................................................................................... 44
2.5.3
Import Substitution Strategy: Implications of a ‘Local Food’ Strategy .................. 47
2.5.4
Nullification of Agricultural Exports ....................................................................... 49
2.6
Conclusion .......................................................................................................... 54
2.7
References .......................................................................................................... 59
3 Determining Water Values with Computable General Equilibrium Models and Application to Southern Nevada ....................................................................................... 62 3.1 Introduction: Computable General Equilibrium Model and the Value of Water in Economic Activity ...................................................................................................... 63 3.1.1
Free, Competitive Market Pricing and Administrative Water Pricing.................... 63
3.1.2
Determining Water Prices among Regions and over Time .................................... 66
3.1.3
CGE Model Basics ................................................................................................... 69
3.1.4
Partial versus General Equilibrium Approaches to Valuing Water in Economies .. 71
3.1.5
When to Use a Water CGE Model .......................................................................... 73
viii 3.1.6
3.2
Challenges Posed by CGE Models in General ........................................................ 73
Water‐CGE Literature Review ............................................................................ 74
3.2.1
Structure of Water‐CGE Models ............................................................................ 80
3.2.2
Applications of Water CGE Models ........................................................................ 89
3.2.3
Special Challenges for Water CGE Models ............................................................. 93
3.2.4
Conclusion ............................................................................................................ 104
3.3
A CGE Model of the Value of Water to Southern Nevada Industry ................. 105
3.3.1
Introduction ......................................................................................................... 105
3.3.2
Southern NV Water CGE Model ........................................................................... 109
3.3.3
Data ...................................................................................................................... 111
3.3.4
Scenarios and Sensitivity Analysis ........................................................................ 117
3.3.5
Results .................................................................................................................. 117
3.3.6
Discussion ............................................................................................................. 122
3.4
Discussion and Conclusions .............................................................................. 126
3.5
References ........................................................................................................ 127
4 Comparing policy advice using a CGE model that operationalizes virtual water flows and water footprints for the state of Nevada ................................................................ 134 4.1
Introduction ...................................................................................................... 134
4.2
Water Footprints, Virtual Water and Water CGE Models ............................... 135
4.2.1
Virtual Water ........................................................................................................ 135
4.2.2
Water Footprints .................................................................................................. 137
4.2.3
Water CGE Models ............................................................................................... 139
4.3
Model Overview ............................................................................................... 141
4.3.1
Production ............................................................................................................ 142
4.3.2
Final Demands ...................................................................................................... 143
4.3.3
Households .......................................................................................................... 144
4.3.4
Government ......................................................................................................... 145
4.3.5
Exports and Imports ............................................................................................. 146
4.3.6
Investment ........................................................................................................... 148
4.3.7
Other Closures ..................................................................................................... 149
4.4
Data and the integration of virtual water and water footprint analysis. ........ 150
ix
5
4.4.1
Money data. ......................................................................................................... 150
4.4.2
Water data. .......................................................................................................... 150
4.5
Virtual water flow and footprint calculations. ................................................. 152
4.6
Simulations ....................................................................................................... 154
4.7
Results .............................................................................................................. 156
4.8
Conclusion ........................................................................................................ 161
4.9
References ........................................................................................................ 163
Summary .................................................................................................................. 167 5.1
Insights provided by this research ................................................................... 167
6
Appendix A. Water Accounts ................................................................................... 172
7
Appendix B. CGE Model Description ....................................................................... 176
x
List of Tables Table 1‐1 Northwest Nevada Average Annual Per Acre Water Application, Evapo‐ transpiration, Returns, and Harvest for 7 Year Rotation .................................................... 7 Table 1‐2 Illustrative Alfalfa Water Use Rates (feet per year) ............................................ 8 Table 1‐3. Relative Prices and Virtual Water Content (VWC) of Joint Products Curds and Whey ................................................................................................................................. 11 Table 1‐4. VWC of Alfalfa as reported by Mubako and Lant ............................................ 12 Table 2‐1. Nevada water withdrawals by county, USGS 2005 ......................................... 23 Table 2‐2. Water Use, Output and Virtual Water Multipliers for NV Industry Sectors .... 31 Table 2‐3. Comparison of final demands and VWC* for local use and for export ........... 33 Table 2‐4. Price change for sector if water is taxed, and consumption rates .................. 36 Table 2‐5. Change in quantity of water consumed if water is taxed ................................ 41 Table 2‐6. Virtual Water Content of $1 million worth of imports and exports ................ 45 Table 2‐7. Domestic Labor‐Capital Aggregate per million $ of Nevada Exports and Import Replacements of 2010 Average Composition ................................................................... 46 Table 2‐8. 2010 estimates of agricultural and food processing imports into Nevada with virtual water content ........................................................................................................ 47 Table 2‐9. Nevada 2010 Water Footprint (AF of Water Consumed) ................................ 48 Table 2‐10. Effects of the nullification of ag exports ‐ I‐O model ..................................... 50 Table 2‐11. Effects of the nullification of ag exports – CGE Model .................................. 51 Table 2‐12. Comparisons of effects of the nullification of agricultural exports in I‐O and CGE models. Percent decrease from baseline, level decrease from baseline .................. 54 Table 3‐1. Clark County Public Water Supply ................................................................. 108 Table 3‐2. 2005 Water Withdrawals by county and sector in southern Nevada. ......... 111 Table 3‐3. Population, resources and resource use by county for southern Nevada .... 112 Table 3‐4. Water use, employment and value‐added by sector for southern Nevada .. 113 Table 3‐5 Three Southern Nevada ranch sale advertisements, 2013 ............................ 115 Table 3‐6. Effect of 10% water supply reduction on raw water rents and treated water prices, with sensitivity analyses ...................................................................................... 118 Table 3‐7 Rental price for raw water ($/AF) ................................................................... 119 Table 3‐8. Sector water withdrawals under baseline total water withdrawals of 910,823 AF and 10% shortage at 819,740 AF total water withdrawals ....................................... 120 Table 3‐9. Change in employment and value added from baseline levels of 1,080,770 and $86,561,000,000. ..................................................................................................... 121 Table 4‐1. Nevada Footprint Water‐CGE exogenously chosen parameters ................... 145 Table 4‐2 Virtual Water Trade Flows .............................................................................. 157 Table 4‐3 Virtual Water Trade Flows, Percent Change from Baseline ........................... 158 Table 4‐4. General Economic Indicators, Percent Change from Baseline ...................... 159 Table 4‐5. Percent Change compared to the Baseline in the Agricultural, Water Utility and Food Processing Sectors .......................................................................................... 160 Table 6‐1. 2005 Nevada Water Withdrawals Adjusted to Water Consumption ............ 173
xi
List of Figures Figure 1‐1. Illustration of virtual water content (VWC) allocation for multiple outputs ... 9 Figure 3‐1. Circular Flow of Income in a Water CGE ........................................................ 70 Figure 3‐2. Water‐CGE papers by time period. ................................................................ 75 Figure 3‐3. Time trends for world water extraction and consumption by sector. ........... 78 Figure 3‐4. Sample of Production Technology .................................................................. 83 Figure 4‐1. Production technology for all sectors excepting water utility ..................... 143 Figure 4‐2. Water utility production function ................................................................ 144 Figure 4‐3 Regional Supply .............................................................................................. 147 Figure 4‐4. Regional Demand .......................................................................................... 148 Figure 6‐1. Modified Blackhurst et al. Method for Deriving Water Intensity Factors .... 175
1
1 1.1
Virtual Water Content of Alfalfa Production in Northern Nevada Introduction Nevada is considered the most arid state in the union, and water rights for an
acre‐foot have sold for as much as $70,000 in the past (Behmaram and Orphan, 2007). In addition, Northwestern Nevada and the rest of the world are facing climate change that may severely impact agriculture and other water intensive industries as well as suburban landscaping. Predictions for Nevada are for less precipitation with more of the remaining precipitation arriving in the form of rain rather than snow. Mountain snow is predicted to run‐off earlier in the spring with less water storage available throughout the summer months (Nevada Climate Change Advisory Committee, 2008). There is a clear potential for negative impacts to Nevada agriculture. Given how dear water is in the state, for all types of uses, it is of interest how much ‘virtual water’ is being trucked outside of the state in the form of hay. In this essay I attempt to measure the extent to which virtual water is embedded in alfalfa hay for Northern Nevada exports. In Nevada as a whole in 2010, an estimated 1,204,000 tons of alfalfa valued at $143 million was grown. Hay is Nevada’s most important crop. Large quantities of alfalfa hay are exported to California and even overseas. An estimated 423 thousand tons of alfalfa was trucked to California in 2010. For the Northern Nevada counties of Washoe, Douglas, Lyon, Pershing, Humboldt and Churchill, alfalfa hay production was 724,000 tons in 2010. Based on state export numbers, I assume about one‐third of the total alfalfa hay production of northern Nevada, or 254,000 tons is exported outside of the
2 state. The rest of the hay is assumed to remain within Nevada and becomes an intermediate input to the beef or dairy industry (National Agricultural Statistics Service, 2009, Owens et al., 2009). The focus of this paper is to estimate the amount of virtual water in the exported alfalfa hay, to discuss its value to the economy of northern Nevada and to start an investigation into how and whether the virtual water and footprint concepts can help inform water policy. 1.2
Virtual Water and Water Footprints What is virtual water? The concept of virtual water is relatively new, with the
term virtual water coined by Allan in 1997 (Allan, 1997). Virtual water is used to denote the water used to produce, though no longer present physically in, a crop or product such as grain or blue jeans. Using a similar concept, Fishelson wrote about the ‘water, food and trade nexus’ with regard to water short countries in the Middle East and North Africa, pointing out that food imports into the arid region greatly reduce demand for water and thus reduce the possibility of conflict over the scarce resource (Fishelson, 1989). Later Allan coined the catchier term ‘virtual water’ in connection with the same phenomenon. Trade in grain and other agricultural goods allows for flows of virtual water around the world, as well as ‘virtual reservoirs’ contained in grain stores and other food supplies. In Nevada, many acre‐feet of water pass by us ‘virtually’ in trucks on their way to California in the form of Nevada raised beef or hay. Many other terms have been used to denote similar ideas; specific water demand, water‐use intensity, unit water requirement, virtual water value, embedded water, embodied water, water‐food‐and‐trade‐nexus, and exogenous water are a few of
3 the terms that have been used. Virtual water and the related water footprint concept can be considered a type of life cycle assessment. There have been various definitions used, but an attempt to write international standards for the water footprint is under way at the International Organization for Standards (Daniels et al. 2011). In this essay, the water footprint of a country or region is defined as the direct water use of the region minus the virtual water content of its exports plus the virtual water content of its imports. The calculations in this essay focus on the water footprint and virtual water content (VWC) definitions provided by the Water Footprint Network (Hoekstra et al., 2009). Water in a water footprint as defined by the Water Footprint Network measures water that is either embodied in a product, or has evaporated or transpired and is no longer available within the region where the production activity is taking place. It does not include returns to ground or surface water within the region.1 That is, virtual water and water footprints measure water consumption rather than water withdrawal. The water footprint can be measured in several dimensions: ‘blue’ indicates water obtained from groundwater or surface water sources, ‘green’ indicates rainwater or ground moisture and grey indicates polluted water. Each dimension could have additional detail included. For example, grey water might be divided up into the types of pollutants. In addition, the time and place water is used may be included (Hoekstra et al., 2009). Van
1
In the case of polluted water, the water does not have to be removed from the region. Grey water measures the amount of water necessary to dilute polluted water enough to meet local water quality standards.
4 Oel, et al., 2009, for example, find that industrial imports of virtual water from China to the Netherlands are composed of 90% grey water and 10% blue water. In terms of the average VWC of specific products, published literature contains studies of coffee and tea in the Netherlands, worldwide cotton consumption, Spanish tomatoes, biomass energy, Spanish grain, worldwide livestock and livestock products and maize amongst others (Chapagain and Hoekstra, 2003, 2007, Chapagain et al., 2006, Chapagain and Orr, 2009a, b, Dabrowski et al., 2009, Gerbens‐Leenes et al., 2009). Chapagain and Hoekstra find that about 140 liters of water are required to grow the coffee needed to make one cup of the beverage, while strong tea only contains an average of 34 liters of virtual water per cup (Chapagain and Hoekstra, 2007). Chapagain and Orr find that the virtual water contained in tomato exports from Spain varies depending on the region and whether the tomatoes are produced in a covered greenhouse or in the open. They estimate nitrate water pollution adds an additional 10% to a grey water component of the virtual water necessary for tomato production (Chapagain and Orr, 2009a). Novo et al. examine the changing levels of virtual water flows from Spanish grain trade in a drought, normal and wet year and find, as expected, more virtual water is imported in dry years when real water is more costly. However, they note that when results are disaggregated into specific types of grains and products that there is less of a coherent pattern in terms of VWC and type of water year. They find that quality, specialization and standardization also drive value and international trade in grain. At the turn of the century, cotton production worldwide contained VWC of about 208 million acre‐feet or the entire average annual flow of the Truckee River
5 370 times over (Chapagain, et al., 2006).2 Of this amount Chapagain et al. found about 39% of the water used was “green” water or rainwater and soil moisture, 42% was “blue” water, or diverted surface water or groundwater and the remaining portion was “grey” water or the amount of water needed to dilute pollutants from growing the cotton to safe levels. Very large amounts of virtual water per pound of product are found in livestock and livestock products because the animal’s entire lifetime consumption of grain and its VWC are contained in the livestock products (Chapagain and Hoekstra, 2003). In the case of world trade in livestock, Chapagain and Hoekstra roughly estimate that it contains 563 million acre‐feet of virtual water, or the equivalent of about 4 and a half times the volume of Lake Tahoe (USGS, 1997). 1.3
Methods According to Wiedmann, there are two basic approaches to calculating the
energy embodied in trade goods (Wiedmann, 2009). I extrapolate this idea to approaches for calculating embodied water as well. The two approaches are: 1. To use a life cycle analysis approach combined with information on physical trade volumes; 2. To use input‐output analysis with trade values in money denominations and economic‐environmental multipliers. Following Hoekstra and Chapagain’s standard methodology, I use the first approach (Hoekstra, et al., 2009). This involves looking closely at the production process of the 2
Truckee River calculation uses average annual flow at the Farad gauge which was 558,700 AF (Truckee Meadows Water Authority, 2003).
6 product under investigation and measuring direct water use for each step as well as indirect water content of each input at each step. For example, to find the VWC of livestock I would calculate how much water the cattle drinks, water needed for cleaning stalls over the lifetime of the cow, as well as the VWC of the grain and pasture grass consumed over the lifetime of the cow (Chapagain and Hoekstra, 2003). Theoretically, one could approach each input in this way, so that a portion of water used to manufacture a tractor used by the farmer to deliver hay would also be added to the total. The water inputs could go back in an infinite series as well. The water used to grow the alfalfa seed used to grow the alfalfa hay fed to the cattle could also be included. In practice, processes are truncated and only inputs that contribute a major portion of water to the production of the product are considered. A typical and idealized process for growing alfalfa in northern Nevada can be found in Curtis et al. 2008. In northern Nevada a stand of alfalfa hay is usually started after a year or two of growing small grains. Establishment takes a year and a half and total stand life is typically six years. As seen in Figure 1‐1, there are two outputs from the alfalfa stand; the hay, which is either baled or cubed, and aftermath grazing (Curtis et al., 2008). In most cases, both the baled alfalfa and the aftermath grazing would not be considered a final product but rather an intermediate product in the production of beef or dairy. However, since we are interested in the export of baled alfalfa from Nevada, it is considered here as a final product. If water is restricted because of drought, a rancher may not apply the ideal amount assumed for four full cuttings as in our idealized crop budget. Thus some of the
7 alfalfa could essentially be using a different production process than our idealized one, for which we do not have the key data on evapo‐transpiration and yield. Applying the ideal production process to the entire hay crop of northwestern Nevada is less reliable to the extent that farmers depart from this ideal crop budget.
0.5 4 4 4 4 4 4 24.5 3.5
0.25 1.3 1.3 1.3 1.3 1.3 1.3 8.1 1.2
0 6.5 6.5 6.5 6.5 6.5 6.5 39 5.6
0 0 2.4 2.4 2.4 2.4 2.4 12 1.7
AF per ton of harvest
Aftermath grazing (tons)
0.5 3.2 3.2 3.2 3.2 3.2 3.2 19.7 2.8
Harvest (tons)
0.25 0.5 0.5 0.5 0.5 0.5 0.5 3.3 0.5
Returns
ET
Year 1* Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Total Average
Rain‐water
Year
AF applied
Table 1‐1 Northwest Nevada Average Annual Per Acre Water Application, Evapo‐ transpiration, Returns, and Harvest for 7 Year Rotation
NA 0.49 0.36 0.36 0.36 0.36 0.36 0.39 0.39
*Work to establish a new stand begins in August
Table 1‐1 displays water use and yields over the seven year rotation of the typical Northern Nevada alfalfa stand. Average water use and yield over the rotation are used to calculate the VWC of the alfalfa stand. About 4 acre‐feet a year of water is applied to the alfalfa on this typical and ideal farm, with the exception of the first establishment year which begins in August. However, some portion of the water runs off or percolates into groundwater supplies. Only the evapo‐transpiration of the water needed for the plant to grow is included in the measurement of VWC (Hoekstra, et al., 2009). Actual evapo‐transpiration of water in alfalfa varies by timing of irrigation, soil type, elevation, temperature and humidity and so will be different in different locations
8 and different years. For example, in Table 1‐2, selected alfalfa annual water consumption ranged from 2.1 feet in 2009 in Deer Lodge, MT to 4.2 feet in 2007 in Glenns Ferry, ID. The lower average rate for Nevada from Eureka is used in the calculations (3.2 AF/year). When averaged over the entire rotation including the partial year when the alfalfa was planted, the average ET per year was 2.8 AF/year. The average harvest over the entire rotation was 5.6 tons per acre of baled or cubed alfalfa and about 4.3 Animal Unit Months (AUMs) of aftermath grazing. This was converted to tons using a factor of 800 dry lbs per AUM giving an average 1.7 tons of aftermath per year (Ruyle and Ogden, 1993). In some water footprint literature, measurement of VWC is divided into “green” and “blue” portions to connote water from rain or soil moisture (green) and water pumped from groundwater storage or diverted from surface water (blue) (Chapagain and Hoekstra, 2004). Given that the data in Table 1‐1 indicates that more irrigation water is applied than is used by the crop in evapo‐transpiration, I assume that all water embodied in the alfalfa constitutes blue water. Table 1‐2 Illustrative Alfalfa Water Use Rates (feet per year) 2007 2008 2009 2010 2011 Average Deer Lodge, MT 2.40 2.25 2.20 2.07 2.33 2.25 Eureka, NV 3.58 3.36 2.85 3.09 3.01 3.18 Fallon, NV 3.98 3.79 3.65 3.59 3.66 3.74 Glenns Ferry, ID 4.18 4.00 3.68 3.40 3.61 3.77 Source: AgriMet, The Pacific Northwest Cooperative Agricultural Weather Network, author’s calculations.
9 Using the highest evapotranspiration rate in the Fallon data (4.0 feet in 2007), the VWC in northern Nevada alfalfa would be 0.57 AF per ton of baled hay. Using the lowest rate from the Eureka data (2.8 in 2009) would give VWC of 0.41 AF per ton. Figure 1‐1. Illustration of virtual water content (VWC) allocation for multiple outputs
Alfalfa Plant Crop Water Requirement = 2.8 AF/year V= 0.39 AF/ton
Average Harvesting Yield = 5.6 ton/acre
4.3 AUM aftermath grazing yield = 1.7 tons/acre
Input to beef or cattle production
When there are multiple outputs from a production process as with the northwestern Nevada alfalfa production illustrated in Figure 1, some way of allocating the VWC is necessary. This is similar to the problem of allocating fixed costs across joint products. Following Hoekstra et al. (2009), a generic formula for calculating the VWC of a product i is:
VWC
Process Water
VWC q q
∗
price ∗ q ∑ price ∗ q
10 Where: Process Wateri is water needed at the split‐off point (in the case of joint products) that produces the output i and the other outputs. y is the number of inputs to the production process and input j has VWCj gives coefficient of production: q units of output produced for each q units of input Pricei gives the market price received for output product i Z is the number of outputs produced by the process at the split‐off point. Some arbitrary rule must be adopted to spread the water use across the different products, in essence assigning responsibility for the water use between consumers of the products. In life cycle accounting, a number of approaches have been taken. According to Flysjo et al. (2011), the International Organization for Standardization (ISO 2006) standards for life cycle accounting suggest a preference order for three general approaches. First, if possible, allocation should be avoided by system expansion, secondly, a physical relationship between inputs and outputs should be used to allocate and then, only if the first two procedures are not possible, an allocation method based on physical or monetary relationships is used. Hoekstra et al. (2009) use the third approach, using a “value fraction” to distribute the acre‐foot of VWC across two or more products: ∑ to distribute the VWC of joint products.
∗ ∗
. The value fraction uses relative prices
11 To see how important this relative price could potentially be in determining virtual water content of a product consider a simple curds and whey example. Suppose a single input, milk, with VWC of 2 AF/ton, is processed so that we have two products, curds and whey. Suppose that 1 ton of milk produces 0.2 tons of curds and 0.8 tons of whey. For simplicity assume processing water needed for the final step is zero. Some proportion of the 2 AF must be distributed to the two products. Using the formula for VWC given above to calculate VWC, Table 2 gives an illustration of how relative prices could change VWC in our curds and whey example, ranging for the curds from 0.3 to 6.9 AF per ton of curds. Since the curds are the focus of production, higher prices for curds will assign it most of the VWC. Table 1‐3. Relative Prices and Virtual Water Content (VWC) of Joint Products Curds and Whey Price of Curds Price of Whey ($/ton) ($/ton) $ 10 $ 90
VWC of Curds (AF/ton) 0.3
VWC of Whey (AF/ton) 2.4
$ 50
$ 50
2.0
2.0
$ 90
$ 10
6.9
0.8
1.4
Results For the representative northern Nevada alfalfa production process, VWC must
be allocated between the baled alfalfa and the aftermath grazing. For the total output I found a VWC of 0.39 AF/ton of alfalfa and aftermath (Table 1‐1). Output prices given in Curtis et al. (2008) are used for the value fraction calculations: $144/ton for baled hay and aftermath grazing at approximately to $43.75/ton. I find the VWC of baled Northwestern Nevada alfalfa to be 0.46 AF/ton and the aftermath to be 0.14 AF/ton. A
12 sensitivity analysis using the price range of baled alfalfa hay over the years from 2008 to 2012 and constant value for aftermath grazing gives a range from 0.44 to 0.48 AF/ton. Variations in evapo‐transpiration rates, give a range from 0.41 to 0.48 AF/ton of VWC per ton of baled northern Nevada hay. For this example, varying evapo‐transpiration rates created more variation (0.41 to 0.48 AF/ton) than did varying the prices. Table 1‐4 gives VWC of alfalfa hay found by Mubako and Lent for various states. The VWC for northern Nevada is close to the national average of 0.50. Table 1‐4. VWC of Alfalfa as reported by Mubako and Lant State
Arkansas
VWC,AF per ton of alfalfa 0.61
California
0.70
Illinois
0.29
North Dakota
0.22
Louisiana
0.33
Texas
0.33
U.S.
0.50 Source: Mubako and Lant (2011), unit conversions by author
Based on state estimates of exports, I assume about one‐third of the total alfalfa hay production of northern Nevada, or 254,000 tons, is exported outside of the state. The rest of the hay is assumed to remain within Nevada and becomes an intermediate input to the beef or dairy industry (National Agricultural Statistics Service, 2009, Owens et al., 2009). With VWC of 0.46 AF/ton, this implies that 118,000 AF/year of virtual blue water is trucked to California in the form of alfalfa. Using the full range of VWC values from the sensitivity analysis gives a range of 105,000 to 145,000 AF/year.
13 To put this amount of water into context, about 112,000 AF was the entire urban demand for water in Reno Sparks region in 2002 (Regional Water Planning Commission, 2005). The combined cities of Reno and Sparks account for most of the population in northern Nevada. 1.5
Conclusion In the most populated region of northern Nevada almost all the water used in
agricultural production first passes through the urban areas of Reno, Sparks, Carson City and Minden and Gardnerville at the eastern edge of the Sierra Mountain range. There is no geographical spatial barrier or extra conveyance cost to urban interests using this water. Any barriers to trade between agricultural and urban interests are primarily institutional.3 Without these institutional barriers one could assume that the market for water is not divided into agricultural and urban segments. Assuming a price range from $90 to $215 per ton, which is the range of prices for Nevada alfalfa from 2008 to 2012 according to the Nevada Agricultural Statistics report, the market value of the exported Northern Nevada alfalfa ranged from $23 to $55 million. Production costs minus aftermath value averaged $114 per ton in the idealized crop budget for Northern Nevada, meaning if these costs are stable, profits could range from ‐$24/ton to $101/ton. Using the highest alfalfa profit, and converting consumption to withdrawal, implies an annual profit per AF of about $175. This would in turn imply a water rights value of about $3500 if interest rates are 5% and there is no appreciation 3
There are some additional institutional barriers with regard to the level of pollutants that are allowed to return to the Truckee River which create costly barriers to increasing certain types of urban water uses in the region.
14 expected in water rights values. A good part of the discrepancy between the $3500 and the $70,000 peak value is explained by the housing bubble and the expectations of increased value. Additional reasons may be lifestyle choices and institutional barriers that prevent sales between rural and urban markets. There may be many reasons for Northern Nevadans to be glad that these water rights remain agricultural water rights. Agricultural water use may be easier to decrease than municipal demand and thus adds to the portfolio of options when drought occurs. In addition, agricultural water may add to Nevadan’s portfolio of options in terms of local food resources. Several aspects of the water footprint and virtual water emerge in this analysis that may be important with regard to the use of this environmental accounting tool in economic modeling. First, the water accounting concerns water consumption rather than water withdrawals. In some economic analyses, this is an unwieldy metric since usually producers and consumers of water will have paid for a withdrawal of water. For example, in the analysis above comparing water rights values it is necessary to revert back to a withdrawal measure to find the appropriate price comparisons. A sensitivity analysis of the VWC contained in northern Nevada hay exports indicated that the value could change by as much as 50% depending on weather conditions. Tracking this type of change might be valuable in comparing relative water use efficiency over time or within regions. In addition, a variation of about 10% in VWC of exports was possible due to variation in world or regional hay prices with respect to local prices for aftermath
15 grazing. The change in VWC due to changes in relative prices is interesting in terms of how these changes could affect policy advice dispensed with regard to virtual water or water footprints. Suppose by‐products, through a different technology, become more valuable. For example, in the case of beef cattle, a production process that makes better use of manures could raise the value of the manure by‐product and reduce the VWC of the beef, changing perceptions about the sustainability of beef production vis a vis water, even though direct water use does not change. Or suppose a change in preferences in favor of local food increases the price of aftermath grazing as compared to hay exports. Recall that a water footprint for a region is the direct water use minus the VWC of exports plus the VWC of imports. The local food preference would have the effect of reducing VWC in hay exports, all else equal, which would increase the local water footprint, all without local direct water use changing. For those who promote greater self‐sufficiency, the footprint would appear worse. For those who promote greater dependence on non‐local agricultural production for arid regions, the water footprint would appear better. In both cases there could potentially be significant change in the opposite direction of expectations due to the recommended value fraction technique for allocation of virtual water to joint products.
16 1.6 References AgriMet: The Pacific Northwest Cooperative Agricultural Weather Network. 2013. "Evapotranspiration Totals and Averages," U.S. Bureau of Reclamation. Allan, J. A. 1997. ""Virtual Water", a Long Term Solution for Water Short Middle Eastern Economies?"," pp. 1‐21. London: School of Oriental and Asian Studies, University of London. Behmaram, V. and Orphan, P. (2007). Washoe County Staff Report, Board Meeting Date: January 16, 2007. Reno, NV: Washoe County Board of County Commissioners. Chapagain, A. K. and A. Y. Hoekstra. 2003. "Virtual Water Flows between Nations in Relation to Trade in Livestock and Livestock Products," In Value of Water Research Report Series No. 13. Delft, The Netherlands: UNESCO IHE Institute for Water Education. ____. 2007. "The Water Footprint of Coffee and Tea Consumption in the Netherlands." Ecological Economics, 64(1), pp. 109‐18. ____. 2004. "Water Footprints of Nations," In Value of Water Research Report Series No. 16. Delft, The Netherlands: UNESCO IHE Institute for Water Education. Chapagain, A. K.,A. Y. Hoekstra,H. H. G. Savenije and R. Gautam. 2006. "The Water Footprint of Cotton Consumption: An Assessment of the Impact of Worldwide Consumption of Cotton Products on the Water Resources in the Cotton Producing Countries." Ecological Economics, 60(1), pp. 186‐203. Chapagain, A. K. and S. Orr. 2009a. "An Improved Water Footprint Methodology Linking Global Consumption to Local Water Resources: A Case of Spanish Tomatoes." Journal of Environmental Management [J. Environ. Manage.], 90(2). ____. 2009b. "An Improved Water Footprint Methodology Linking Global Consumption to Local Water Resources: A Case of Spanish Tomatoes." Journal Of Environmental Management, 90(2), pp. 1219‐28. Curtis, Kynda,Mimi Kobayashi and Carol Bishop. 2008. "Northwestern Nevada Alfalfa Hay Establishment, Production Costs and Returns, 2008," Reno, NV: University of Nevada Cooperative Extension, University of Nevada, Reno. Dabrowski, J. M.,E. Masekoameng and P. J. Ashton. 2009. "Analysis of Virtual Water Flows Associated with the Trade of Maize in the Sadc Region: Importance of Scale." Hydrology and Earth System Sciences, 13(10).
17 Daniels, Peter L.; Manfred Lenzen and Steven J. Kenway. 2011. "The Ins and Outs of Water Use ‐ a Review of Multi‐Region Input‐Output Analysis and Water Footprints for Regional Sustainability Analysis and Policy." Economic Systems Research, 23(4), pp. 353‐ 70. Fishelson, Gideon ed. 1989. "Economic Cooperation in the Middle East." Westview Special Studies on the Middle East Boulder, Colo. and London: Westview Press, 372, pp. 372. Flysjo, Anna; Christel Cederberg; Maria Henriksson and Stewart Ledgard. 2011. "How Does Co‐Product Handling Affect the Carbon Footprint of Milk? Case Study of Milk Production in New Zealand and Sweden." International Journal of Life Cycle Assessment, 16, pp. 420‐30. Gerbens‐Leenes, P. W.,A. Y. Hoekstra and Th van der Meer. 2009. "The Water Footprint of Energy from Biomass: A Quantitative Assessment and Consequences of an Increasing Share of Bio‐Energy in Energy Supply." Ecological Economics, 68(4), pp. 1052‐ 60. Hoekstra, A. Y.,A. K. Chapagain,M. M. Aldaya and M M. Mekonnen. 2009. "Water Footprint Manual: State of the Art 2009," Enshade, Netherlands: Water Footprint Network. Mubako, Stanley and Christopher Lant. 2011. "Virtual Water Trade and Water Footprints of U.S. States." Downloaded July 3, 2013 from http://www.cwi.colostate.edu/UCOWR‐NIWR/files/UCOWR_Presentations/23_‐ _Water_Economics/Christopher_Lant/Virtual‐Water‐US.pdf National Agricultural Statistics Service. 2009. "2007 Census of Agriculture Nevada State and County Data," Washington, D.C.: United States Department of Agriculture. Nevada Climate Change Advisory Committee. 2008. "Governor Jim Gibbons’ Nevada Climate Change Advisory Committee Final Report," In. Carson City, NV: Office of the Governor. Owens, Marty,Don Gephart,Mark Deonier and Candace Lucero. 2012. "Nevada Agricultural Statistics 2011," Reno, NV: Nevada Field Office, National Agricultural Statistics Service. Also, 2010,2009. Regional Water Planning Commission. 2005. "2004‐2025 Washoe County Comprehensive Regional Water Management Plan," Reno, NV: Washoe County Department of Water Resources.
18 Ruyle, George and Phil Ogden. 1993. "What Is an AUM?," In Arizona Rancher's Management Guide. Tucson, AZ: Arizona Cooperative Extension. Truckee Meadows Water Authority. 2003. "2005‐2025 Water Resource Plan," Reno, NV. United States Geological Survey. 1997. Stream and Ground‐Water Monitoring Program, Lake Tahoe Basin, Nevada and California, FS‐100‐97. Downloaded August 19, 2013. http://pubs.usgs.gov/fs/FS‐100‐97/ Wiedmann, Thomas. 2009. "A First Empirical Comparison of Energy Footprints Embodied in Trade‐‐ MRIO Versus PLUM." Ecological Economics, 68(7), pp. 1975‐90.
19
2 2.1
Dietzenbacher and Velazquez 2007 Revisited: Analyzing Nevada Virtual Water Trade with Input‐Output and CGE Models Introduction The Great Recession and structural problems in the Nevada economy have
produced a pause in the high population growth rate experienced over the last several decades. This growth created a strong demand for water rights within the region, and pushed prices per acre‐foot up to $70,000 in 2006 at the peak of the growth bubble (Behmaram and Orphan, 2007). The current pause may be the appropriate time to thoroughly examine water availability and distribution issues. The input‐output (IO) framework for virtual water trade presented in Dietzenbacher and Velazquez (2007) allows us to understand more fully which sectors and which final demands use water and how much they require both directly and indirectly. It also helps us track how much virtual water is exported and how much is imported, a feature that may be of use to understanding issues regarding ‘local food’ production versus imports of food. To understand the full implications of any policy change is difficult within the I‐O framework, however, so the framework is extended for use with a CGE model. Virtual water may be especially important to examine where there are barriers to water markets which distort water prices. Distorted water prices may create inefficiencies and water may not flow to its highest and best use. For example in Northern Nevada, a pipeline has been discussed which would import groundwater to Reno from up to 125 miles away in Humboldt Co., itself a very arid region. The pipeline has been projected to cost $160 million dollars or more (Wollan, 2007). Similarly, in
20 southern Nevada, a 300 mile long pipeline priced at $3.5 billion is planned from rural water sources in Northern Nevada and Utah (Lippert and Efstathiou, 2009). The pipeline projects imply a value for water that may not be realized through all types of agricultural production. Ultimately, the pipelines could end up being paid for by public utility customers. In this context, it is worthwhile to carefully examine exports of virtual water. Is export of water intensive agricultural commodities Nevada’s best use of limited water resources? Would it be better if agricultural water were diverted for municipal use? What would happen if those same water resources were used for local food production? The term virtual water, first published by Allen (1997), is used to denote all the water used to produce, though no longer present physically in, a crop or product such as grain or blue jeans. Both direct use and indirect use of the water is accounted for. Virtual water generally refers to water actually consumed by evapotranspiration or physical incorporation into the product and not to total water withdrawals.4 For example, in a study of water used in the production of soft drinks most of the virtual water content (VWC) was attributed to agricultural inputs such as sugar while process water at a bottling plant did not count towards total VWC since almost all of the water was discharged to the sewer and recycled in the region.5 The highly related concept of the water footprint measures virtual water flows for products, companies, households 4
Daniels et al., 2011.
5
Ercin, A Ertug; Maite Martinez Aldaya and Arjen A. Hoekstra. 2011. "Corporate Water Footprint Accounting and Impact Assessment: The Case of the Water Footprint of a Sugar-Containing Carbonated Beverage." Water Resour Manage, 25, pp. 721-41.
21 or regions. For example, the water footprint of a country is the direct water use of the country plus the VWC of imports minus the VWC of the exports. Where input‐output techniques are used to find them, the virtual water and water footprint concepts are in the tradition of environmental and resource multipliers in I‐O models (Harris and Ching, 1983, Leontief, 1970). A method to account for virtual water use is outlined by Hoekstra et al. (Hoekstra et al., 2009). The method described has most often been used to find virtual water flows at the national level but has also been adapted to the provincial level. In Bulsink et al. (2010), for example, this method is applied to Indonesia. Using a bottom‐ up process oriented approach, Bulsink et al. find that the province of Java is highly dependent on the virtual water embodied in imports from other provinces. Water used for producing major agricultural commodities is estimated using precipitation, evaporation and transpiration rates, and information on acreage by crop, as well as other detailed information about livestock and dairy production. Trade flows between regions are estimated using the assumption that local needs are met first. This technique has the advantage of incorporating detail about specific agricultural commodities but the disadvantage of not including non‐agricultural sectors. In addition, indirect water use of inputs a few steps upstream in the production process are truncated (Daniels et al., 2011). Dietzenbacher and Velazquez demonstrate a top‐down methodology that depends on the use of input‐output tables for the Andalusian region of Spain. Their method uses the top‐down approach with less attention to sectoral detail but includes water use from all sectors. The additional data needed to analyze
22 virtual water is a vector of total water use by sector. Using this data, water consumption per Euro of output is calculated as well as virtual water multipliers. Virtual water multipliers give the direct and indirect water consumption required per dollar of final demand. The I‐O framework includes data on imports and exports from other regions. Using this data, flows of virtual water can be calculated. I follow Dietzenbacher and Velazquez and calculate the virtual water flows into and out of the state of Nevada using the I‐O framework. The use of an I‐O method then provides the base data for an extension to a CGE model which allows for more nuanced exploration of policy alternatives than is available with the I‐O model results alone. The following section lays out the general geography of Nevada with regard to water availability and use. Part three presents the input output framework, the CGE model and the data. The fourth section gives the results of the analysis of the Nevada data with the framework suggested by Dietzenbacher and Velazquez. Water consumption amongst final demand users, including exports, is examined in comparison to its proportion of dollar value. Results of Dietzenbacher and Velazquez’ imposition of a water tax are compared for the I‐O framework and the CGE model. Leontief’s exercise in comparing the factor intensity of imports and exports is applied to Nevada for the case of water resource use intensity, finding that in the case of Nevada, the Heckscher‐Ohlin theory of trade for relatively water intensive trade goods is not contradicted. The implications of this for local food import substitution are discussed. Following Dietzenbacher and Velazquez, the consequence of decreasing exports of different
23 sectors is also examined with the I‐O framework, but followed by analysis with the CGE model. Results are compared. The final section consists of concluding remarks. 2.2
Geography and Water Use in Nevada Brown et al. estimated the average annual water endowment of Nevada
(precipitation minus evapotranspiration within state boundaries) for the period from 1953 to 1994 to be 4,700,515 AF (Brown et al., 2008). Using this estimate with USGS estimates of water withdrawals (see Table 2‐1), nearly 57% of average annual water endowment is diverted for human uses. Depending on the specific use of the withdrawn water, anywhere from about 30% to 85% of the water may be returned to the region. Table 2‐1. Nevada water withdrawals by county, USGS 20056 County Name CHURCHILL CLARK (Las Vegas) DOUGLAS ELKO ESMERALDA EUREKA HUMBOLDT LANDER LINCOLN LYON MINERAL NYE PERSHING STOREY WASHOE (Reno/Sparks) WHITE PINE CARSON CITY TOTAL
Total Withdrawals (AF) 200,842 680,756 58,270 399,757 46,755 94,798 295,572 120,225 57,060 243,923 14,876 76,808 147,680 4,212 130,900 79,609 11,112 2,663,153
% of Total Withdrawals 7.5% 25.6% 2.2% 15.0% 1.8% 3.6% 11.1% 4.5% 2.1% 9.2% 0.6% 2.9% 5.5% 0.2% 4.9% 3.0% 0.4% 100.0%
6
United States Geological Survey. 2010. "Estimated Use of Water in the United States County‐Level Data for 2005," U. S. Department of the Interior.
24 The state is over 110,000 square miles, however, so summary measures do not adequately address regional water availability within Nevada. Most of the population of Nevada (72%) lives in Las Vegas and its surrounding suburbs in Clark County. This population receives about 90% of its water endowment from the Colorado River, which originates outside of Nevada and which must serve even larger downstream demands in Arizona, California and Mexico (Lippert and Efstathiou, 2009). Hundreds of miles to the north, the urban population of Reno in Washoe County and the counties surrounding Washoe make up another 22% of the population (Hardcastle, 2013). This northern area receives most of its water endowment from either the Truckee or Carson River and related groundwater (Regional Water Planning Commission, 2005). The Truckee and Carson rivers originate in the Sierra Nevada mountains in California, west of Nevada and end in Pyramid Lake, a terminal lake and the Stillwater marshes within in Nevada. A large amount of the water in these two rivers is reserved to preserve wildlife and fisheries and to meet Native American water rights. Most water scarcity pressure is related to these two urban regions. Clark and Washoe Counties account for about 31% of total state water withdrawals (Table 2‐1). Economic activity in the urban regions includes a large casino hotel and gambling sector. Water withdrawals are spread throughout the state, with the agricultural sector being by far the largest rural user of water. Mining and electricity generation are also heavy water users in rural areas. Controversial inter‐basin water pipelines to bring agricultural water from distant rural areas into the two highly populated regions have been under consideration.
25 2.3
Description of I‐O Framework, CGE Model and Data
2.3.1 IO framework
To find virtual water flows it is necessary to account for both direct and indirect water consumption. Leontief originally attempted to carry out the inter‐industry input output framework with physical units of materials. Although this proved difficult, the method has remained useful for tracing such things as energy, pollutants or water used or emitted during production processes. The framework used in this paper is described in Dietzenbacher and Velazquez, 2007 and uses a traditional approach also described in Miller and Blair (2009) as the most straightforward of the environmental accounting extensions to Leontief’s I‐O method.7 This method uses a separate matrix (in this application, a vector) of water intensity flows by sector, given in terms of direct water use per dollar of output. The framework does not include direct water use by final demand institutions, since the primary interest is movement of virtual water embodied in agricultural and other trade goods. The elements of the I‐O framework are as described in Dietzenbacher and Velazquez and are applied to aggregated Nevada IMPLAN data described below:
A 20 by 20 matrix of inter‐industry coefficients, A
A 20 by 8 matrix of final demands, F. Final demands include three income levels of households, government sectors and investment, foreign and domestic exports
A 20 by 1 vector of outputs, x
7
Miller and Blair, 2009. P. 400.
26
V, a 4 by 20 matrix of value added
M, a 20 by 20 matrix of inter‐industry imports
γ, a 20 by 1 vector of water use per dollar of output.
The classic I‐O question is, given a final demand vector, f, what level of output, x, will be necessary to produce the final demand? The classic equation for this problem is: which can be solved for x yielding: is the Leontief matrix, L. To solve the similar problem, given f how
where
much direct and indirect water consumption is required to produce it, the analogous equation is: ′ where is a vector which gives total direct and indirect water use by sector. Data for inter‐industry transactions and trade flows was taken from the IMPLAN database for Nevada for the year 2010 (Minnesota IMPLAN Group, 2011).8 IMPLAN relies on data from a variety of federal government databases such as the Bureau of Labor Statistics Quarterly Census of Wages and Employment, the Census Bureau County Business Pattern data, Census of Population and the Annual Survey of Manufacturing, the Census of Agriculture, Bureau of Economic Analysis Regional Economic Information 8
This database was modified so as to better represent the Nevada economy and for use as the water CGE database. The water utility sector, originally primarily within the state and local government sector in IMPLAN, was moved so that all activity takes place in the water utility sector. Small amounts of institution sales of agricultural commodities were re‐characterized as imports. Also, estimates of hay imports were reduced so that local demands are met first by local supply.
27 System and more. The 440 industry sectors in IMPLAN were aggregated to 20 sectors. Commodity by industry and trade accounts were converted into industry by industry accounts using the industry‐based technology assumption.9 Some final payments to other property income were moved to a water final payments row to connote rents to water rights owners in the CGE model. See essay 3 for more detail. There are three different options for determining trade flows in version three of IMPLAN. The trade flows used in this analysis are from the IMPLAN national trade flows model. This model is described in Lindall et al. (2006). A doubly constrained gravity model which uses IMPLAN derived commodity supply and demand is used to estimate inter‐county level imports and exports. This model also uses data from the Oak Ridge National Labs on county to county distances and Commodity Flows Survey data by commodity. No data exists on the movement of services between regions within the United States, so services are estimated using a parameter on the gravity model that constrains services to be delivered primarily to its closest customers. The water use intensity factors were estimated using a method suggested by Blackhurst et al. (Blackhurst et al., 2010a, b) and modified for use with state level IMPLAN data. The method and its adaptation are described in Appendix A. Water use in this analysis is estimated water consumption, except where noted. United States
9
Everything an activity produces is assumed to use the same recipe. Thus if the hay sector produces both hay and vegetables, the same inputs of production are assumed to be used for both activity outputs. See p. 192 Miller and Blair, 2009 Miller, Ronald E. and Peter D. Blair. 2009. Input‐Output Analysis:Foundations and Extensions Cambridge, UK: Cambridge University Press.
28 Geological Survey (USGS) return rate estimates are applied to each sector (Smith et al., 2011) to find an estimate for water consumption given water withdrawals. 2.3.2 CGE Model
The CGE model uses the same data as the I‐O framework with the same 20‐ sector aggregation, in general. However, there are some differences between the two datasets. The CGE model contains additional data which completes the necessary social accounting matrix. The CGE model retains the use and make table for modeling prior to calculation of the virtual water multipliers. Additional IMPLAN data on sales of commodities by institutions, as well as transfers between institutional sectors, completes the social accounting matrix used to calibrate the CGE model, while additional USGS water use estimates for direct use by households is used to help apportion initial water use by institutions. The Washington State University regional CGE model (Stodick et al., 2004) was adapted to incorporate water as a factor of production. Instead of having one type of water as is the case in the I‐O framework, the CGE model has both ‘raw’ and treated water. The raw water is a factor of production in the agricultural, mining, electrical and water utilities, food processing and manufacturing sectors. The water utility sector purchases raw water which is used to produce treated water purchased by other industry sectors and by institutions. Total water use and water intensity factors used to calculate the virtual water content remain the same for both the I‐O framework and the base CGE model however. Unlike the I‐O framework, the CGE model incorporates a constraint on total water availability equal to the current demand for water in the I‐O
29 framework. It also allows for substitution of inputs between water, labor and capital in response to relative prices as well as changes in consumer demand in response to relative prices, again, unlike the I‐O framework described above. These features allow for greater flexibility and realism when modeling water policy. A representative firm for each of the 20 sectors maximizes profit subject to a nested constant elasticity of substitution (CES) production function. The water utility sector, unlike other sectors, must use water in fixed proportions to output to ensure that at least one unit of raw water factor is used for each treated unit of water produced. Total water supply is fixed but is mobile between sectors. Labor supply responds to wages and is fully mobile between sectors. Capital is fixed and sector specific. These closures approximate a short‐or moderate term model. Factors are rented from three representative households; one each for low, medium and high income level households. These households maximize Stone‐Geary utility functions subject to the budget constraint given by the income from these factor rents as well as government transfers. The Stone‐Geary function allows for subsistence levels of consumption to be specified for water utility, agricultural and other sector commodities. Savings is investment driven. There are two levels of government. State and local government is combined into one government level. This level of government collects the water taxes. A fixed portion of indirect business taxes also goes to the state and local government and it spends tax revenue in fixed proportions. It is required to have a balanced budget. The federal government is allowed to run a deficit. Nevada is assumed to be a small open economy that is a price taker in import and export markets. Imports
30 and exports are treated with the standard Armington assumption and are considered imperfect substitutes for locally produced goods and services from the same sector and are substituted or transformed according to a CES and CET functions. For fuller documentation of the model and its equations, see section 4.3 and Appendix B. 2.4
Direct and Indirect Water Use by Sector
Following Dietzenbacher and Velazquez, Table 2‐2 displays the vector of outputs, x, the vector of water use per dollar, γ, and the total direct water use per sector, w. Also displayed in Table 2‐2 are the virtual water multipliers, ε γ’* I‐A ‐1, which give the water requirements for a unit of final demand by sector, as well as the ratio of the virtual water coefficient to the direct water coefficient, εi/γi. The same data is used for the base CGE model and I‐O framework. Virtual water multipliers for the I‐O model and base CGE model are the same. Most of the direct water use occurs in the agricultural sector despite its relatively small value of output. Other heavy direct water using sectors are mining and electricity. When comparing the direct water use coefficient to the virtual water multiplier agricultural final demand uses relatively little extra water through indirect water use with the exception of the livestock sector. In contrast, the food processing sector requires more than 250 times the direct and indirect water consumption for a unit of final demand than a unit of processed food output requires directly in production of output. This is because one of its largest inputs is agricultural produce. Construction sector final demand also is a relatively heavy user of water when indirect use is considered. On average, virtual water in final demand is about four times the direct
31 water use in output for service sectors, and three and a half times direct water use in output for manufacturing. Table 2‐2. Water Use, Output and Virtual Water Multipliers for NV Industry Sectors Direct water Virtual Total direct use water Ratio, Value of output, water use, coefficients, multipliers, εi/γi xi ($) wi (AF) γi* εi** 2086.9 395,312,439 824,996 2115.0 1.0 421.0 233,321,417 98,232 784.4 1.9 370.2 106,081,312 39,269 534.7 1.4 209.1 85,862,306 17,950 218.3 1.0 570.9 88,804,654 50,699 576.5 1.0 909,382,128 1,031,146 1133.9 1236.3 1.1 2.1 1,199,180,430 2,496 7.5 3.6 4.6 6,061,269,020 27,813 8.0 1.8 18.6 1,702,551,076 31,623 18.7 1.0 0.0 825,412,436 0 1.0 NA 0.1 9,940,198,269 839 3.5 41.2
Sector HAY LIVESTOCK DAIRY VEG & MELON OTHER AG Ag subtotal Average OTHER MINING METAL MINING ELECTRICITY WATER UTILITY CONSTRUCTION RESIDENTIAL CONSTR. FOOD PROCESSING MANUFACTURING Manufacturing subtotal Average
0.1 0.2 0.2
1,785,402,441 2,225,962,737 10,811,531,937
34,551,508,346 1.9
TRANSPORT&UTIL BANKS,INSUR.,REAL ESTATE TRADE OTHER SERVICES HEALTHCARE RECREATION FOOD&DRINK Services subtotal Average
0.1
11,476,660,639
0.0 37,883,994,699 0.1 16,325,126,301 0.2 42,214,069,317 0.1 9,686,593,042 0.5 22,203,405,611 0.2 8,494,554,741 148,284,404,350 0.2
Grand total Grand average
*AF/million $ output **(AF/million $ final demand)
183,745,294,824 6.1
121 391 1,866 65,150 980 1,588 1,136 6,335 1,032 10,547 1,949 23,568 1,119,863
4.9 72.6 44.5 253.3 0.6 3.5 6.6
3.5
0.3
3.3
0.6 0.3 0.4 0.4 1.1 1.7
14.2 4.6 2.9 3.3 2.4 7.5
0.7
4.1
6.1
1.0
32 2.5
Results Using the virtual water multipliers, I compare the virtual water content (VWC) of
the final demand sectors. VWC of product i for final demand sector k is calculated as: VWCik fik*εi, where fik is the final demand for product i in final demand sector k. In Table 2‐3 each sector and super‐sector’s percentage of total final demand is given. These can be compared with the sector’s percentage of total VWC. For example, exports outside of the region are 42% of total final demand but contain 83% of the total VWC. The agricultural sector accounts for only about one half of one percent of total final demand. Over 80% of this is for export. This means that 69% of total VWC of final demand is embodied in agricultural exports. Final demand for local manufacturing and for export are almost an even split, with VWC somewhat higher in exports. Services make up the largest portion of final demand. VWC for services is small and is about evenly split between local demand and exports. 2.5.1 Imposition of a Water Tax Dietzenbacher and Velazquez show how the virtual water multiplier gives the price increase of the product in sector i if water prices are increased or equivalently, taxed.10 I follow their demonstration and Miller and Blair to give this cost‐push interpretation of the I‐O model. In an input‐output model, one set of accounting equations sets total output (which is also equal to total outlay) equal to total input 10
Dietzenbacher, Erik and Esther Velazquez. 2007. "Analysing Andalusian Virtual Water Trade in an Input‐Output Framework." Regional Studies, 41(2), p. 190‐191.
33 costs.11 This reflects zero profit for firms when the economy is in equilibrium. Thus for each sector j, we have x
z
m
v
Table 2‐3. Comparison of final demands and VWC* for local use and for export Sector HAY LIVESTOCK DAIRY VEG & MELON OTHER AG Agriculture subtotal OTHER MINING METAL MINING ELECTRICITY WATER UTILITY CONSTRUCTION RESIDENTIAL CONSTR. FOOD PROCESSING OTHER MANUFACTURING Mfg. subtotal TRANSPORT&UTIL BANKS,INSUR., REAL ESTATE TRADE OTHER SERVICES HEALTHCARE RECREATION FOOD&DRINK Services subtotal Grand total
Local final demands 0.01% 0.03% 0.00% 0.04% 0.01% 0.08% 0.01% 0.70% 0.45% 0.43% 5.69%
0.19% 0.10% 0.04% 0.02% 0.05% 0.40% 0.71% 3.21% 0.11% 0.01% 0.37%
0.20% 0.13% 0.04% 0.05% 0.05% 0.47% 0.72% 3.91% 0.56% 0.44% 6.06%
VWC* local 1.84% 2.62% 0.01% 1.03% 0.44% 5.93% 0.01% 0.73% 1.09% 0.05% 2.55%
1.22% 0.47%
0.02% 0.90%
1.24% 1.37%
0.77% 2.69%
0.67% 9.64% 2.50%
6.27% 11.60% 2.65%
6.94% 21.23% 5.14%
0.05% 0.49% 0.55% 7.94% 10.11% 18.05% 0.09% 0.10% 0.19%
13.03% 7.96% 14.42% 6.60% 1.34% 2.72% 48.56% 58.28%
4.33% 2.12% 4.83% 0.04% 13.42% 2.34% 29.73% 41.72%
17.36% 10.08% 19.25% 6.64% 14.76% 5.06% 78.29% 100.00%
1.00% 0.33% 1.33% 0.33% 0.09% 0.41% 0.80% 0.27% 1.06% 0.30% 0.00% 0.30% 0.19% 1.94% 2.13% 0.61% 0.52% 1.13% 3.31% 3.24% 6.55% 17.19% 82.81% 100.00%
Exports
Total
VWC* exports 52.36% 10.44% 2.65% 0.46% 3.55% 69.46% 0.69% 3.32% 0.25% 0.00% 0.17%
VWC* total 54.20% 13.07% 2.65% 1.49% 3.99% 75.39% 0.69% 4.05% 1.34% 0.06% 2.71%
0.01% 5.18%
0.79% 7.87%
*Virtual water content
11 Miller, Ronald E. and Peter D. Blair. 2009. Input-Output Analysis: Foundations and Extensions Cambridge, UK: Cambridge University Press, pp. 43-47.
34 where zij are the elements of the matrix of sales and purchases between industries. xj represents the value of output for sector j, mij is purchase of import i by industry j, and vfj is amount of factor f needed in industry activity j. This accounting equation, which is in accord with constant returns to scale and perfect competition, requires the value of output be equal to the value of the inputs to production: intermediate inputs, imports and value added components such as labor and capital. The usual assumption in input‐ output models is that the unit of x is chosen such that the price is equal to one. Dividing through by xj we have 1
z /x
m /x
v /x
or p
1
a
m /x
v /x
where the a are the elements of A, the matrix of inter‐industry coefficients. This equation can be interpreted as giving unit costs for sector j. Considering all sectors at once, in vector notation this can be written as: where ek is a vector of ones of length k, N is the number of industries and F is the number of factors. Rearranging and substituting the Leontief matrix L for I‐A ‐1 we have: ′
35 Now suppose that there is a new tax, t, for water use. The new cost of a unit of production from sector j, p p
, will be: p
a
m /x
v /x
w /x
where w is the new cost of water in sector j. As we are imposing a per unit water tax, w
t ∗ w where w is the total number of units of water required in sector j for
output xj. Rewriting in vector notation we have: ′
Thus the changes in price due to the new water tax are: ′
t ’
t
which is the tax multiplied by the virtual water multipliers. For example, if water consumption were taxed at $10 per acre‐foot in the case of the Nevada, the unit price of hay would increase by 2%, and the unit price of livestock would increase by almost 1%. The unit prices of other agricultural goods would increase modestly, while price increases in other sectors would be negligible (Table 2‐4). Dietzenbacher and Velazquez state that full exploration of the imposition of a water tax would require “demand elasticity’s or a computable general equilibrium (CGE) model. . .”.12 This is because the water tax or price increase would change relative prices which in turn would change equilibrium quantity demanded and supplied for water as well as other factors and goods. The I‐O model does not allow for this price response. Accordingly, I use a CGE model to explore the imposition of a similar water tax. This is 12
Ibid. p. 191.
36 complicated because of water being both a factor and an intermediate input. To get close to replicating the IO model tax both the factor and intermediate input must be Table 2‐4. Price change for sector if water is taxed Sector HAY LIVESTOCK DAIRY VEG & MELON OTHER AG OTHER MINING METAL MINING ELECTRICITY TRANSPORT&UTIL WATER UTILITY CONSTRUCTION RESIDENTIAL CONSTR. BANKS,INSUR., REAL ESTATE FOOD PROCESSING OTHER MANUFACTURING TRADE OTHER SERVICES HEALTHCARE RECREATION FOOD&DRINK Water factor costs to producer inclusive of tax Water factor rents Industry water withdrawals Industry water consumption Employment Value‐added
IO Model 2.115% 0.784% 0.535% 0.218% 0.576% 0.008% 0.008% 0.019% 0.000% 0.001% 0.003% 0.005% 0.001% 0.045% 0.001% 0.000% 0.000% 0.000% 0.001% 0.002% 10.152% 0.0% 0.000% 0.000% 0.000% 0.000%
Scenario 1*
Scenario 2**
0.007% 0.005% 0.002% 0.002% 0.001% 0.000% 0.000% 0.003% 0.002% ‐0.427% 0.007% 0.001% 0.004% 0.001% 0.001% 0.002% 0.005% 0.002% 0.000% 0.002%
0.245% ‐0.124% ‐0.096% 0.016% 0.013% 0.000% ‐0.001% 0.005% 0.003% ‐0.425% 0.008% 0.003% 0.005% 0.001% 0.000% 0.002% 0.005% 0.002% 0.000% 0.002%
0.00% ‐11.064% ‐0.073% ‐0.078% 0.009% ‐0.003%
0.00% ‐10.948% ‐0.073% ‐0.645% 0.009% ‐0.003%
*Scenario 1: CGE model with water tax based on average water consumption rate **Scenario 2: CGE Model with a sector specific water consumption tax rate
37 taxed. In the IO model, there is only one kind of water consumed, measured by the direct water coefficients given in AF per million $ of output, γ.13 In the CGE model there are two types of water, ‘raw’ water purchased as a factor of production, and treated water, purchased as a commodity from the water utility sector. A unit water tax is imposed on the raw water factor and the treated water commodity separately. The tax on intermediate demand for treated water, t
,
, is an additional cost paid by the
firm for each unit of treated water, driving a wedge between the price received by the water utility sector for a unit of water, PQ t
treated water, who will pay PQ state government of ∑ t
, and the firms which purchase the
,
,
QINT
,
. The tax generates revenue for the
where QINTwater‐c,a is the quantity of
treated utility water demanded by the producer as intermediate inputs to activity a. Similarly, the unit tax on the raw water factor, tufwater,a, is an additional cost paid by the firm for each unit of raw water purchased from factor owners at water factor rent, WF
,
. The tax generates revenue for the state government of ∑ tuf
,
QF
,
where QFwater,a is the quantity of raw water demanded by the producer as a factor input to activity a. The taxes are incorporated into the CGE model in the zero profit conditions, PA
PQ
t
,
ica
,
WF ,
tuf ,
V,
where ica is the inter‐industry coefficient parameter which gives quantity demanded of commodity c in production activity a (inter‐industry inputs are combined in a Leontief 13
To avoid double‐counting of water use, the water utility’s water consumption is not counted.
38 aggregate in the model), and V , gives the variable quantity of factor f required for a production of one unit of activity a. Demand for the treated water commodity has been specified as a fixed proportion to output, so no further specification of the commodity tax is required. However, for the raw water factor, firms must maximize profit subject to CES production functions. Factor demand equations derived from the specified production functions are:
WF ,
PVA ad vashare
tuf ,
δ , QF ,
δ , QF ,
where PVA
PA
PQ
t
,
ica , ,
and vashare is the share of total outlay spent on intermediate inputs that are not factors, ada is a shift parameter, δf,a is a share parameter and ρa is a CES exponent. Two policy scenarios are modeled. Industries in the CGE model must pay for their raw water withdrawals, not their water consumption. In order to account for this difference between the I‐O framework and the CGE model, in the first scenario the tax is imposed on water withdrawals reduced by the average rate of water return for either treated water or raw water for the entire economy: tuf t
,
,
0.6009 ∗ t
0.1704 ∗ treatwater ∗ t
39 Treatwater is the conversion factor from units of treated water commodity to acre‐ feet. Each industry sector pays the same tax rate based on their water withdrawals. In the second scenario the tax is based on water consumption by sector. In this scenario industry sectors pay differing water tax levels based on their estimated return flows with those who return more water paying a lower tax rate on their withdrawals. See Table 2‐5 for estimated consumption rates by sector.14 Thus, the tax rate on withdrawals differs depending on return rates for the sector: tuf t
, ,
CONVERT ∗ t, CONVERT ∗ treatwater ∗ t.
CONVERT is the consumption rate for industry a. No tax is imposed on the water utility sector for raw water factor use in any of the scenarios. The tax revenue is collected by the state government and increases state government spending proportionally. Results are given in Tables 2‐4 and 2‐5. Sector price changes for the three approaches are given in Table 2‐4, and display three quite different patterns. For the I‐O model, the taxes on the water input are paid by the producing industry sector and the cost of an industry consuming an acre‐foot of water increases 10%. The customers for their goods and services do not respond to changes in price. The consumers have infinitely inelastic demand, and therefore cost increases in the water factor are ultimately passed forward and completely borne by purchasers of the commodities. All sector output prices are forced up due to the tax in proportion to the importance of virtual water in the production process. The hay sector 14
The estimates are derived from USGS estimates from Smith et al. 2011. See Appendix A for more detail.
40 experiences the largest increase in the price of its outputs (2.1%), followed by a 0.8% increase in the price of livestock industry outputs and modest increases in other agricultural sector goods. Price increases in the rest of the economy occur but are negligible. There are no decreases in price. In contrast, in the CGE model for scenario 1 with tax imposed across the board on average consumption, the water factor is supplied inelastically and this forces the tax increase to be borne by the owners of the water factor, reducing their rents. In the CGE model, the rent received by water factor owners decreases by 11%. Some subtle changes in the price of output take place due to the tax, but they are very small, with the water utility commodity tax creating the largest change (‐0.4%). For the water utility sector, which does not get directly taxed on its water factor input, the sales tax on its product does not outweigh decreased rents paid to factor owners for water factor inputs. Treated water prices decrease and demand increases despite the sales tax on its product. In scenario 2, the inelastic supply of the water factor again means that factor owner’s water rent decreases by 11%. However, since water taxes now reward industries with higher water return rates, these differential tax rates do noticeably change industry output prices, both increasing and decreasing prices. Livestock and dairy sectors return more of the water that they use than do other agricultural sectors, and the price of their output decreases slightly by about 0.1% despite the 0.2% increase in the price of the hay input.
41 Table 2‐5. Change in quantity of water consumed if water is taxed and consumption rates
Scenario 1
HAY LIVESTOCK DAIRY VEG & MELON OTHER AG OTHER MINING METAL MINING ELECTRICITY TRANSPORT&UTIL WATER UTILITY CONSTRUCTION RESIDENTIAL CONSTR. BANKS,INSUR., REAL ESTATE FOOD PROCESSING OTHER MANUFACTURING TRADE OTHER SERVICES HEALTHCARE RECREATION FOOD&DRINK TOTAL
‐0.08% ‐0.11% ‐0.06% ‐0.04% ‐0.06% ‐0.05% ‐0.04% ‐0.04% 0.00% NA 0.02% 0.00% 0.00% ‐0.01% ‐0.02% 0.00% 0.01% 0.01% 0.00% 0.00% ‐0.08%
Scenario 2 ‐1.48% 3.86% 3.16% ‐0.97% ‐1.21% 4.31% 4.35% ‐2.07% 0.00% NA 0.02% 0.00% 0.00% 1.05% 3.05% 0.00% 0.01% 0.01% 0.00% 0.00% ‐0.65%
Consumption rate 67% 37% 37% 67% 67% 27% 27% 76% 15% 22% 15% 15% 15% 15% 15% 15% 15% 15% 15% 15% 55%
The water tax policy is put in place in hopes of driving more efficient use of water. The way that the water tax effects water use is also quite different in the two different models and in each of the CGE scenarios. The cost push approach with the I‐O framework holds quantities constant as prices change, and thus is of little value in evaluating the water tax policy’s effect on water use. Both CGE model scenarios assume that the supply of water for withdrawal is supplied inelastically and in both models there is no change in total water withdrawals when considering the economy as a whole inclusive of final demand. However, total industrial water consumption does decrease by 0.08% in the first scenario, and by a somewhat larger amount in scenario 2 (0.65%)
42 where taxes are applied differentially. Both scenarios bring about positive and negative changes in sectoral water use, but the differential tax brings about much larger more discernable changes. Sectors that have higher rates of water return increase their water use while heavier water users must decrease water use. For the CGE model results, in scenario 1, where taxes are applied according to the amount of water withdrawn, there is a very slight increase in employment and no discernable change in the value added for Nevada’s economy. The second scenario produces more change in the production mix but still has little effect on employment or value added. The largest negative change in the Nevada economy is the decrease in value‐added of 0.003% in scenarios 1 and 2 (Table 2‐4). The tax creates some distortions but little changes in the economy. Again, the nature of the cost‐push analysis in the I‐O framework does not allow any specific predictions about what changes could take place across the Nevada economy since quantities are held constant as a part of the methodology. In both CGE model approaches, the consequences of the water tax are small for the Nevada economy overall, but factor owners bear almost the entire tax burden. The consequences for overall water use are modest, with municipal water use actually increasing under the specific taxes modeled in these simulations.
The CGE model results are clearly more reflective of real word conditions in most
cases than the cost push approach using the I‐O framework, for several reasons. First, the price responsiveness and substitution behaviors allowed for with the CGE model are clearly more reflective of a real economy. By its assumptions, the I‐O cost‐push framework cannot reveal to us how water consumption by sector would change in
43 response to a tax. Secondly, although the inelastic supply of water specified in the CGE model is not completely realistic, it is much closer to reality than the infinitely elastic supply posited in the typical I‐O framework. Lastly, the comparison of the I‐O framework with the CGE model points to an interesting dilemma for the economic modeling of virtual water and water footprints. In calculations of virtual water content, it has generally been agreed that water consumption is the correct dimension of measurement. The consumed water could be ‘green’ water, which is soil moisture provided by rainfall, or ‘blue’ water, which is water withdrawn from ground or surface water supplies. Consumption is the portion of the green and blue water that evaporates, transpires or otherwise is removed from the region in question. The CGE model, however, must reflect financial transactions based on amounts of blue water withdrawn for use, since that is what is paid for by industries in Nevada and in most regions. For the two CGE scenarios, a tax could more likely be imposed on water withdrawals than on water consumption, especially, for agricultural water users where return flows are difficult to measure. Thus the results found in scenario 1 are the most likely result of a water tax. The water factor owners would bear the burden of the tax while having little effect on water use. Of course, the CGE model in this essay may also have a flawed representation with regard to modeling incentives due to the water tax. In the model, factor owners and producers are separate entities. However, in Nevada, and many other regions, factor owners and producers in agriculture are often the same entity. Incentives to change Nevada water use patterns may accordingly be different than those modeled with the current version of the Nevada Water CGE.
44 2.5.2 Leontief Experiment According to the Heckscher‐Ohlin trade theory, regions export the good which requires the relatively abundant factor of production relatively intensively. One might expect Nevada would export water thrifty products and import water intensive products, since Nevada’s arid climate should mean it does not have a comparative advantage in relatively water‐intensive goods. Following Leontief’s famous experiment which found the United States to be exporting labor‐intensive goods rather than capital intensive goods (Leontief, 1953), and Dietzenbacher and Velazquez, I compare the virtual water content in $1,000,000 worth of Nevada exports and $1,000,000 worth of Nevada imports. The million dollars of exports and imports are assumed to mirror the sectoral distribution of total exports and imports. Virtual water content (VWC) of producing both is calculated as before. The VWC of imports is calculated, following Leontief’s technique, by assuming that the imports would have to be replaced using the current Nevada production functions. The results are given in Table 2‐6. One million dollars of exports from Nevada contain 16% less virtual water (15 AF) than does $1 million of imports (18 AF) if they had to be produced in Nevada. For the agricultural subsector, import and export VWC is relatively similar with high VWC exported in hay balanced out by high VWC imported in “OTHER AG” which includes all agricultural products other than hay, livestock, dairy, vegetables and melons. For manufactured items, high VWC in metal mining exports is far outweighed by Nevada imports from the food processing sector. As might be expected, high exports of the relatively water intensive recreation sector, which
45 includes casino hotels, makes Nevada services exports higher in VWC than its service imports. In balance, however, Nevada is clearly following Heckscher‐Ohlin trade theory by exporting more water‐thrifty products than it imports. Dietzenbacher and Velazquez acknowledge that without looking at two factors of production they cannot know whether Andalusia truly has comparative advantage without assuming that it has relatively less water than its trading partners when Table 2‐6. Virtual Water Content of $1 million worth of imports and exports Sector
HAY LIVESTOCK DAIRY VEG & MELON OTHER AG Agriculture subtotal OTHER MINING METAL MINING ELECTRICITY WATER UTILITY CONSTRUCTION RESIDENTIAL CONSTR. FOOD PROCESSING OTHER MANUFACTURING Manufacturing subtotal TRANSPORT&UTIL BANKS,INSUR.,REAL ESTATE TRADE OTHER SERVICES HEALTHCARE RECREATION FOOD&DRINK Services subtotal Grand total *Virtual water content
Distribution of $1 million in exports $ 4,611 $ 2,480 $ 922 $ 391 $ 1,146 $ 9,550
VWC* Distribution of $1 VWC* exports million in imports imports (AF) (AF) 9.75 $ 3,298 6.98 1.95 $ 2,055 1.61 0.49 $ 1,052 0.56 0.09 $ 1,435 0.31 0.66 $ 6,398 3.69 12.94 $ 14,238 13.15
$ 16,994 $ 76,839 $ 2,538 $ 255 $ 8,917 $ 447 $ 21,676 $ 150,259 $ 277,925
0.13 0.62 0.05 0.00 0.03 0.00 0.96 0.09 1.88
$ 25,565 $ 19,350 $ 17,932 $ 21 $ 13,841 $ 99 $ 90,783 $ 408,397 $ 575,989
0.19 0.16 0.34 0.00 0.05 0.00 4.04 0.25 5.02
$ 63,459 $ 103,890 $ 50,828 $ 115,688 $ 889 $ 321,641 $ 56,130 $ 712,525 $ 1,000,000
0.02 0.06 0.02 0.05 0.00 0.36 0.10 0.60 15.42
$ 80,549 $ 77,956 $ 27,478 $ 171,446 $ 31,591 $ 19,968 $ 785 $ 409,773 $ 1,000,000
0.02 0.05 0.01 0.07 0.01 0.02 0.00 0.19 18.36
46 to other factors of production.15 To truly test the Heckscher‐Ohlin trade theory, we would need to know the abundance of water relative to other factors of production and have these ratios available for Nevada’s trading partners as well. Leontief’s experiment does not use information about trading partners, however, but only the relative ratios of factor inputs for exports and import replacements were they to be made within the United States. Leontief examined both the labor and capital ‘embodied’ in his vectors of traded goods, not just one or the other. To complete Leontief’s experiment I use a ratio of a labor‐capital aggregate input with the water factor input for imports and exports. The embodied labor‐capital content is found using the same methodology used to find virtual water content. To approximate his experiment, I compare embodied water to an aggregate of embodied capital and labor, reproducing Leontief’s table for these two inputs in Table 2‐7 (Leontief, 1953). Unlike Dietzenbacher and Velazquez in Spain, I find that Nevada exports and imports do conform to the Heckscher‐Ohlin trade theory. Exports from Nevada have relatively more labor and capital embodied in them than virtual water. Table 2‐7. Domestic Labor‐Capital Aggregate per million $ of Nevada Exports and Import Replacements of 2010 Average Composition Labor‐Capital Aggr ($) Water (AF)
Import Replacements $ 692,023 $ 616,072 15.4 18.4 Exports
Ratio 1.12 0.84
15
P. 195, Dietzenbacher, Erik and Esther Velazquez. 2007. "Analysing Andalusian Virtual Water Trade in an Input‐Output Framework." Regional Studies, 41(2), pp. 185‐96.
47 2.5.3 Import Substitution Strategy: Implications of a ‘Local Food’ Strategy Nevadans imported an estimated $6.9 billion worth of agricultural and food processing sector commodities in 2010 (see Table 2‐8). The virtual water content (VWC) of these commodities, if they had to be replaced using current Nevada technology, is estimated from the model to be nearly 1.1 million acre‐feet, 14% more virtual water than is contained in all the exports of Nevada (Table 2‐9). Net Nevada virtual water imports are estimated to be 273 thousand AF with 18% of the total Nevada water footprint falling outside the region. For local food demands to be met entirely locally Table 2‐8. 2010 estimates of agricultural and food processing imports into Nevada with virtual water content % of total Food and Ag % of total VWC of food VWC of food Imports (millions Food and Ag and ag imports and ag of $) imports (AF) imports HAY $ 215.6 3% 456,048 41% LIVESTOCK $ 134.3 2% 105,376 9% DAIRY $ 68.7 1% 36,758 3% VEG & MELON $ 93.8 1% 20,487 2% OTHER AG $ 418.3 6% 241,134 21% FOOD $ 5,935.0 86% 264,177 24% PROCESSING TOTAL $ 6,865.8 100% 1,123,980 100% would require changes in industry size. The size of local food processing and agricultural sectors would have to expand. In addition, preferences would likely need to change so that local replacements of currently imported foodstuffs were available.16 If it is assumed that water in Nevada is already completely allocated, then it would be difficult 16
The model is currently aggregated in such a way that there are no non‐comparable imports. However, Nevada clearly has many non‐comparable imports of agricultural products at a more disaggregated level.
48 to completely meet local food needs locally, given the constraint on water supply, merely by forgoing production of exports. Changes in technology, preferences or both would need to occur. Local food advocates are currently encouraging a variation of the ‘buy local’ import substitution strategy, hoping to promote economic development and wise resource use, amongst many other goals. For example, one report by a local food supporter’s coalition advocates increasing the proportion of food obtained locally by Table 2‐9. Nevada 2010 Water Footprint (AF of Water Consumed) Nevada 2010 water footprint summary table 1 Direct institution water use
Base (AF) 146,301
2 Industry use
1,119,863
3 Total direct water use
1,266,164
4 VWC exports
927,408
5 VWC imports
1,200,323
6 Footprint (1+2‐4+5)
1,539,080
Net imports VWC (6‐3) Per capita footprint
Percent of footprint falling outside of region
272,916 0.58 18%
25% as a step towards full self‐sufficiency in food (Masi et al., 2010). Given that Nevada would seem to be using its comparative advantage in water thrifty goods and exporting less water intensive goods than it is importing, a local foods strategy may be disadvantageous to the state economy. If one assumes there are no externalities or other distortions in the NV economy, then the current optimum mix of imports is one with more water intensive products than the average optimum mix of exports from
49 Nevada. If based on the current average production mix, a ‘local food’ import substitution strategy might prove unwise unless it can be shown that there are agricultural sectors which can produce commodities of equal or greater quality at equal or lower cost. In addition, the VWC of imports compared to exports indicates that there would not be enough water to produce the current mix of imports within Nevada. Less water is used to produce exports than is used to produce imports. Again, if we assume that there are no externalities and competitive markets for water, interventions increasing local food production may imply a less efficient allocation of water resources. However, both short term and long term water markets as well as agricultural markets are well‐known to have distortions. Also, the I‐O framework is descriptive only. It cannot model the price responsiveness that would help to make an adaptation to a local foods strategy possible. 2.5.4 Nullification of Agricultural Exports Again following Dietzenbacher and Velazquez, I investigate the effect on the Nevada economy of nullifying the exports of the agricultural sector but compare the results obtained from the I‐O framework and the CGE model. Suppose Nevada were to cease exporting any primary agricultural goods. The results of this decrease in exports are shown in Table 2‐10 for the I‐O model and in Table 2‐11 for the CGE model. In the CGE model, demand for agricultural exports is driven to zero by specifying an exogenous downward shift in demand outside the region such that Nevada agricultural exports are no longer competitive:
50 PER
,
PWE
,
∗ 1
shift
,
where PERc,t is regional price received for exports of commodity c and region t, PWEc,t is the exogenous price paid for exports by purchasers outside the region and shiftc,t is the specified shift in demand. Table 2‐10. Effects of the nullification of ag exports ‐ I‐O model Sector
HAY LIVESTOCK DAIRY VEG & MELON OTHER AG Agriculture subtotal OTHER MINING METAL MINING ELECTRICITY WATER UTILITY CONSTRUCTION RESIDENTIAL CONSTR. FOOD PROCESSING OTHER MANUFACTURING Manufacturing subtotal TRANSPORT&UTIL BANKS,INSUR.,REAL ESTATE TRADE OTHER SERVICES HEALTHCARE RECREATION FOOD&DRINK Services subtotal Grand total
Final Water Demand (consumption) savings
Value Added
Employment Effects
‐97.28% ‐82.09% ‐99.81% ‐30.79% ‐94.06% ‐85.61%
‐78.92% ‐74.78% ‐52.56% ‐27.64% ‐88.27% ‐77.09%
‐78.92% ‐74.78% ‐52.56% ‐27.64% ‐88.27% ‐69.20%
‐78.92% ‐74.78% ‐52.56% ‐27.64% ‐88.27% ‐76.15%
Trade Balance (millions of $) ‐$148 ‐$85 ‐$34 ‐$16 ‐$55 ‐$338
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% 0.00%
‐0.04% 0.00% ‐0.45% NA ‐0.04% 0.00% ‐0.13%
‐0.04% 0.00% ‐0.45% ‐0.64% ‐0.04% 0.00% ‐0.13%
‐0.04% 0.00% ‐0.45% ‐0.64% ‐0.04% 0.00% ‐0.13%
$0 $0 $1 $1 $1 $0 $2
0.00%
‐0.06%
‐0.06%
‐0.06%
$3
0.00%
‐0.22%
‐0.08%
‐0.06%
$7
0.00%
‐0.14%
‐0.14%
‐0.14%
$3
0.00%
‐0.22%
‐0.22%
‐0.22%
$7
0.00% 0.00% 0.00% 0.00% 0.00% 0.00% ‐0.41%
‐0.10% ‐0.07% 0.00% ‐0.01% ‐0.01% 0.05% ‐71.00%
‐0.10% ‐0.07% 0.00% ‐0.01% ‐0.01% ‐0.10% ‐0.27%
‐0.10% ‐0.07% 0.00% ‐0.01% ‐0.01% ‐0.08% ‐0.37%
$1 $2 $0 $0 $0 $14 ‐$316
51 Table 2‐11. Effects of the nullification of ag exports – CGE Model Sector
Value Final Water Demand (consumption) Added savings
HAY LIVESTOCK DAIRY VEG & MELON OTHER AG Agriculture subtotal
‐96.62% ‐79.13% ‐97.95% ‐32.29% ‐88.56% ‐83.84%
‐92.37% ‐50.30% ‐95.57% 193.06% ‐51.62% ‐77.40%
‐90.67% ‐90.91% ‐92.51% ‐49.08% ‐96.86% ‐86.13%
0.17% 0.21% 0.13% 0.13% ‐0.03% 0.02% 0.32%
232.57% 328.70% 419.71% 151.81% 151.85% 152.79% 131.12%
0.06% 0.20% ‐0.53% ‐0.02% ‐0.03% ‐0.01% 0.64%
0.08% 0.21% ‐0.35% 1.54% ‐0.03% ‐0.01% 0.54%
$3 $12 $9 $0 $0 $0 $15
0.06%
224.07%
0.01%
0.01%
$36
0.05%
207.78%
‐0.15%
0.05%
$75
0.13%
151.81%
‐0.02%
‐0.01%
$0
OTHER MINING METAL MINING ELECTRICITY WATER UTILITY CONSTRUCTION RESIDENTIAL CONSTR. FOOD PROCESSING OTHER MANUFACTURING Manufacturing subtotal
Employment Trade Effects Balance (millions of $) ‐89.13% ‐$240 ‐89.40% ‐$124 ‐87.37% ‐$50 ‐40.09% ‐$22 ‐95.95% ‐$50 ‐88.84% ‐$486
TRANSPORT&UTIL BANKS,INSUR.,REAL ESTATE TRADE OTHER SERVICES HEALTHCARE RECREATION FOOD&DRINK Services subtotal
0.26%
156.22%
0.04%
0.03%
‐$8
0.18% 0.04% ‐0.05% 0.05% 0.23% 0.12%
191.40% 119.72% 149.89% 144.57% 141.78% 145.83%
0.10% 0.10% 0.41% 0.08% 0.25% 0.10%
0.09% 0.09% 0.39% 0.07% 0.23% 0.10%
‐$1 ‐$7 $1 $2 $3 ‐$11
Grand total
‐0.29%
‐11.29%
‐0.16%
‐0.24%
‐$423
The resulting changes in final demand, value‐added, employment and trade balance equilibrium values are reported in Table 2‐11. Results for the CGE model and I‐O
52 framework cannot be precisely parallel for several reasons. The CGE model has a full make and use table including some institution sales of commodities while the I‐O model, though using the same initial make and use values, has been simplified. Water in the CGE model is tracked both as a raw water factor and as a treated water commodity, while in the I‐O framework, total water use is tracked with water use coefficients and to avoid a double‐count the water utility uses no water. However, these additional differences are representative of the two different approaches and their strengths and weaknesses. For the I‐O framework, define value‐added multipliers ∗ ∗
where is a diagonal matrix with 1/xi as the diagonal elements. Then VMi*fik is the value added impact of nullification of fik. In this case k denotes final demand for exports (rather than home region final demand). Employment impacts can be defined in a very similar way. If exports decline, imports will also decline and their decrease can be also be found in a similar way. The trade balance in sector j will change by 1‐IMPMi * x ∗ I
A
*fik, where IMPMi denotes the proportion of outlay spent on imports for the
ith industry. In the I‐O model, the final demand ‘shock’ is the nullification of agricultural exports, and affects only those sectors. Water consumption decreases by 77% in the agricultural sector and as fewer inputs are needed throughout the economy, small decreases in water use in other sectors are experienced as well. Just as water supply expands elastically in the I‐O model, so does it contract elastically. The end result is a
53 71% decrease in industry water consumption in the economy. Because water is used in fixed proportions, as agricultural sectors contract, water use simply contracts as well. In contrast, in the CGE model, water is supplied inelastically with price bringing supply and demand back into balance after the shift in demand for agricultural exports occurs. As water prices decrease, the high value vegetable and melon sector uses even more water despite reducing production levels. Similarly, other sectors begin to substitute cheap water for other factors of production. As labor and water are released from the agricultural sector, other industries are able to use these now cheaper inputs to expand operations. Industry water consumption only decreases by 11%. Although there is a decrease in industry water withdrawals and in consumption levels across the economy, there is no decrease in water withdrawals when considering the entire economy as final demand sectors buy more of the cheaper water. Instead price brings the market back into equilibrium. As agricultural sectors demand less water, the price of water decreases by over 90% and intermediate and final demand for water increases in other sectors. The easing of water constraints are signaled by the decrease in price rather than an overall decrease in quantity demanded or supplied. For the I‐O framework, total final demand decreases by less than half a percent ($594 million), total value added decreases by 0.27% ($313 million) and employment by 0.37% (5,498 jobs). Total exports, value‐added and employment all decrease from about 30% to 40% more than they do in the CGE model since the CGE model allows other sectors to increase activity as water and labor are freed from agricultural sectors and
54 now available to other sectors. However, the trade balance worsens considerably more in the CGE model. Although the negative impacts of the decreases in agricultural production are small relative to the entire Nevada economy, they are large enough to matter, especially given that the effects would not be spread out over many sectors or regions but would be concentrated primarily in alfalfa and beef sectors in small rural communities. In general, although the CGE may have allowed a higher elasticity of substitution between water and labor and capital than is realistic, it illustrates an important point. Although the impacts of nullifying agricultural exports are softened by the greater flexibility allowed for in the model as compared to theI‐Oframework, the aim of the nullification, namely, water saving, is also dampened. Any fairly large decrease in water use by the agricultural sector will tend to dampen water prices and allow for increased use in other sectors and by other final demands. Table 2‐12. Comparisons of effects of the nullification of agricultural exports in I‐O and CGE models. Percent decrease from baseline, level decrease from baseline Final Water savings Demand I‐O Model ‐0.41% ‐71.00% CGE Model ‐0.29% ‐11.29% Difference (%) ‐29.32% ‐84.10%
2.6
Value added ‐0.27% ‐0.16% ‐42.83%
Employment Trade balance effects (million $) ‐0.37% ‐$316 ‐0.24% ‐$423 ‐34.83% 33.81%
Conclusion An enormous share of Nevada water is used in order to produce agricultural
products for export. However, using a Leontief style experiment to compare, I find that relatively more abundant capital and labor factors are embodied in exports than are
55 embodied in imports. Although Nevada’s trade does not violate Heckscher‐Ohlin trade theory in that there is relatively lower virtual water content in exports than in imports as compared to labor and capital, it is still surprising that such an arid region would export such a large amount of water intensive agricultural product. Although the virtual water trade flows in the expected direction, water use is still not necessarily rational. When the economic value from virtual water exports are compared to pipeline costs, there may still be a question as to whether such exports are the best use for Nevada’s limited water supplies.
I find that Nevada is a net importer of virtual water. Eighteen percent of
Nevada’s water footprint lies outside of the state. Given current technology and preferences, the Leontief experiment and the footprint calculation imply that substantial increases in production of local food might be difficult, since the current optimum mix of imports favors a water‐intensive mix that could not be replaced by giving up the current mix of exports. Marginal changes in increased local food consumption remain viable. Local foods movements are interested in meeting many goals, many of them associated with externalities or perceived market failures. One of these might be food security over a long period of time with fluctuating world food and energy prices, or under conditions of global warming with fluctuating weather conditions. Other goals are in addressing various environmental and health externalities. Whether or not these goals can be met is something that unfolds over time with clear expectations that there
56 will need to be changes in preferences and changes in technology that can facilitate the local food production sector. The outcome of a tax imposed on water is examined using both the CGE model and the I‐O framework. The CGE model is superior for policy analysis. Water supply is more likely to resemble the inelastic supply specified in the CGE model than the infinitely elastically supplied I‐O framework. The I‐O cost‐push framework results in all tax increases being passed on to the consumer because by assumption consumers and producers in the framework have no response to price increases. In general, where water policy is an issue, water supplies will be constrained, making the I‐O model results, with its assumption of unlimited factor resources, inapplicable. The CGE model could relatively easily be adapted to allow some level of supply response to changes in prices for water by specification of a water supply function with elasticity between zero and infinity. The results of the CGE model tax policy experiment may indicate the flaws in such a water tax as a policy to encourage water reallocation or water use reduction. Since the tax was passed on to water factor owners, total water use and allocation did not change appreciably. However, the CGE model may falsely separate water factor owners and agricultural producers since they may actually be the same entity. A tax that changed relative prices of water by rewarding sectors that have higher return flows was shown to reduce water consumption despite the assumption of inelastic demand.
Often, because returns to water are low in some agricultural sectors, arid regions
may feel pressure to abandon agriculture in favor of other types of economic activity with higher rate of returns to water. A simulation of nullification of agricultural exports
57 from Nevada in the CGE model and I‐O framework both show that such a change, though devastating to the agricultural sectors and rural regions, would have a fairly small negative effect on the economy overall. In the more realistic CGE model, even these small negative effects are somewhat dampened by movement of factors of production into other industry sectors. However, the CGE model predicts that the goal of more efficient water use would not be well served, since as agricultural water needs decrease, the price of water plummets and water is used in place of other factors of production and in place of other consumption items by households. The lower water prices in the model send a message of water abundance to the market rather than a message of water scarcity. The use of the virtual water content and water footprint concepts helped describe how much total water is consumed to produce final demand, especially for the export sector, and especially for the meat, dairy or food processing sector that indirectly use large quantities of water. The footprint findings are suggestive: virtual water flow in exports is a river that might be drawn from at relatively low expense. In fact these waters have been drawn from repeatedly in the past as agricultural water rights are converted to municipal or other types of water rights. But it is not entirely clear how these descriptive findings can be used for policy prescription. In fact their ability to suggest directions for policy without incorporating economic principals of opportunity costs and multi‐input production and consumption may actually be more harmful than helpful.
58 To model alternative water policies the I‐O framework used for finding the virtual water flows is not adequate. The water‐CGE model can more fully compare policy outcomes for the water tax increase and the nullification of agricultural exports. Footprint and virtual water flows concepts are not used when comparing water tax impacts with the water CGE model. The virtual water multipliers derived from input‐ output are used to compare the results of the nullification of exports using CGE results. They provide a different perspective on water use (change in total direct and indirect water use by sector) but in my example have been used primarily so that the CGE results compare with the input‐output results rather than for any policy purpose. The water‐ CGE can directly provide the change in water use across the entire economy given a change in exports. In this case there is no need for a footprint type approach. Thus the water footprint approach has proved to be of most value in description of direct and indirect water use, not in any direct policy analysis in this paper. The comparison of the water taxes in I‐O and CGE models revealed another aspect of the footprint concept that is awkward to use with economic modeling: the emphasis on water consumption, including green water. Financial transactions more typically link with blue water withdrawals. Green water may perhaps link to land value, but the link might be difficult to measure. Return flows are not often specified in water rights, and financial transactions will not typically take these into account. Further development of water‐CGE models and these links is a good area for future research.
59 2.7 References Allan, J. A. (1997). “’Virtual water’, a long term solution for water short Middle Eastern economies?". London: School of Oriental and Asian Studies, University of London, pp. 1‐ 21. Behmaram, V. and Orphan, P. (2007). Washoe County Staff Report, Board Meeting Date: January 16, 2007. Reno, NV: Washoe County Board of County Commissioners. Blackhurst, Michael; Chris Hendrickson and Jordi Sels i Vidal. 2010a. "Direct and Indirect Water Withdrawals for U.S. Industrial Sectors." Environmental Science and Technology, 44(6), pp. 2136‐30. ____. 2010b. "Direct and Indirect Water Withdrawals for U.S. Industrial Sectors, Supplemental Material." Environmental Science and Technology, 44(6), pp. 2136‐30. Brown, Thomas C.; Michael T. Hobbins and Jorge A. Ramirez. 2008. "Spatial Distribution of Water Supply in the Coterminous United States." Journal of the American Water Resources Association, 44(6), pp. 1474‐87. Bulsink, F., Hoekstra, A. Y. and Booij, M. J. (2010). "The water footprint of Indonesian provinces related to the consumption of crop products." Hydrology and Earth System Sciences 14: 119‐128. Daniels, Peter L.; Manfred Lenzen and Steven J. Kenway. 2011. "The Ins and Outs of Water Use ‐ a Review of Multi‐Region Input‐Output Analysis and Water Footprints for Regional Sustainability Analysis and Policy." Economic Systems Research, 23(4), pp. 353‐ 70. Dietzenbacher, Erik and Esther Velazquez. 2007. "Analysing Andalusian Virtual Water Trade in an Input‐Output Framework." Regional Studies, 41(2), pp. 185‐96. Ercin, A Ertug; Maite Martinez Aldaya and Arjen A. Hoekstra. 2011. "Corporate Water Footprint Accounting and Impact Assessment: The Case of the Water Footprint of a Sugar‐Containing Carbonated Beverage." Water Resour Manage, 25, pp. 721‐41. Hardcastle, Jeff. 2013. "Nevada State Demographer 2012 Estimates," Reno, Nevada: College of Business Business Service Group University of Nevada Reno. Harris, Thomas R. and Chancey T. K. Ching. 1983. "Economic Resource Multipliers for Regional Impact Analysis." Water Resources Bulletin, 19(2), pp. 205‐10.
60 Hoekstra, A. Y.; A. K. Chapagain; M. M. Aldaya and M M. Mekonnen. 2009. "Water Footprint Manual: State of the Art 2009," Enshade, Netherlands: Water Footprint Network. Kenny, J.F.; N.L. Barber; S.S. Hutson; K.S. Linsey; J.K. Lovelace and M.A. Maupin. 2009. "Estimated Use of Water in the United States in 2005," United States Geological Survey. Leontief, Wassily. 1953. "Domestic Production and Foreign Trade: The American Capital Position Re‐Examined." Proceedings of the American Philosophical Society, 97(4), pp. 332‐49. ____. 1970. "Environmental Repercussions and the Economic Structure: An Input‐ Output Approach." The Review of Economics and Statistics, 52(3), pp. 262‐71. Lindall, S., Olson, D. and Alward, G. (2006). "Deriving Multi‐Regional Models Using the IMPLAN National Trade Flows Model." Journal of Regional Analysis and Policy 36(1): 76‐ 83. Lippert, John and Jim Jr. Efstathiou. 2009. "Las Vegas Running out of Water Means Dimming Los Angeles Lights," Bloomberg.com. New York, N.Y.: Bloomberg. Masi, Brad ; Leslie Schaller and Michael H. Shuman. 2010. "The 25% Shift: The Benefits of Food Localization for Northeast Ohio & How to Realize Them," In. Cleveland, OH: Cleveland Foundation, ParkWorks, Kent State University Cleveland Urban Design Collaborative, Neighborhood Progress Inc., Cleveland‐Cuyahoga County Food Policy Coalition. Miller, Ronald E. and Peter D. Blair. 2009. Input‐Output Analysis: Foundations and Extensions Cambridge, UK: Cambridge University Press. Minnesota IMPLAN Group (2009). "Implan Professional Version 3.0." Stillwater, Minnesota. Mubako, Stanley. 2011. "Frameworks for Estimating Virtual Water Flows among U.S. States," In Department of Environmental Resources and Policy, 251. Carbondale, IL: Southern Illinois University. National Agricultural Statistics Service. 2009. "2007 Census of Agriculture Nevada State and County Data," Washington, D.C.: United States Department of Agriculture. ____. 2008. "2008 Farm and Ranch Irrigation Survey," United States Department of Agriculture.
61 Regional Water Planning Commission. 2005. "2004‐2025 Washoe County Comprehensive Regional Water Management Plan," In. Reno, NV: Washoe County Department of Water Resources. Smith, C.A.; A.J. Simon and R.D. Belles. 2011. "Estimated Water Flows in 2005," Ed. Lawrence Livermore National Lab. Livermore, CA. Statistics Canada. 2010. "Industrial Water Use: 2007," Ottawa, Canada. Stodick, Leroy; David Holland and Stephen Devadoss. 2004. "Documentation for the Idaho‐Washington CGE Model," School of Economic Sciences, Pullman, WA: Washington State University. United States Geological Survey. 2010. "Estimated Use of Water in the United States County‐Level Data for 2005," U. S. Department of the Interior. Wollan, M. (2007). "In Race to Find Water, It’s Science vs. “Witchers." Wall Street Journal, August 3, 2007.
62
3
Determining Water Values with Computable General Equilibrium Models and Application to Southern Nevada
A previous version of this paper was submitted to Industrial Economics, Inc. for presentation at “The Importance of Water to the U.S. Economy: Technical Workshop, September 19, National Academy of Public Administration, 900 7th Street NW, Suite 600, Washington D.C. The previous report was submitted by co‐authors Elizabeth Fadali, Kimberly Rollins, and Shawn Stoddard and is available at http://www.indecon.com/iecweb/PolicyWaterImportance.aspx .
63 This paper explains and demonstrates the use of computable general equilibrium (CGE) models for determining the value of water in economic activity. In this essay I review existing CGE models that formalize water use, (2) discuss how CGE models should be built to model water‐related economic changes and policy questions, and (3) construct a model that demonstrates some of these findings. The paper is organized as follows: first, I discuss why water markets are generally not competitive and how this complicates both water resource management planning and the modeling of water for economic uses. Next I describe the basic elements of CGE models that focus on water by way of a literature review of published CGE papers that incorporate water. The third section describes a ‘water CGE’ model of southern Nevada developed to demonstrate the above concepts. Finally, the concluding section summarizes advantages and limitations of CGE modeling of water in general and for the southern Nevada application in particular. 3.1
Introduction: Computable General Equilibrium Model and the Value of Water in Economic Activity
3.1.1 Free, Competitive Market Pricing and Administrative Water Pricing A well‐functioning competitive market results in prices that equate social values with social costs for freely transacted quantities. Every water customer would compete with – thus ultimately pay the same price (net of conveyance costs) as—every other water customer. And, at that competitively determined price, the quantities of water used by each firm would be the quantities at which the price they pay just covers the value of the water they use (the marginal value product, MVP).
64 This free and competitive market generally does not exist in the United States. Water ‘prices’ do not reflect the marginal social cost or the marginal social value of water. For treated water, the prices charged for water are usually administratively set, with the utility companies who bear the costs of acquisition, treatment, storage, conveyance, and distribution subject to subsidies and regulations. Most water utilities are publicly owned (Rourke, 2009). The quantities of water transacted are also regulated rather than freely chosen. In particular, geographically and temporally limited water stocks and flows are administratively apportioned to various categories of users according to legal water rights institutions. Particularly for firms in the agricultural sector (the major water user), the quantity of water used is not freely determined. Societies everywhere have long chosen to manage water use and to administratively set the price and quantities used, for good reasons. One, because of technical scale economies, regional water utilities are natural monopolies (Wittwer 2012; Young 2005). In the absence of government regulation, the profit incentive would lead utilities to sell less water at higher prices compared to the free, competitive market. Two, water is essential for life. Although it is usually not prohibitively expensive to exclude someone or something (plants, animals) from consuming water, it can be morally unacceptable to do so. Thus water effectively has a nonexcludability attribute akin to that of a public good. All else equal, in the absence of government regulation too much water would be consumed by those who can pay (humans) leaving too little for those who cannot pay (such as fauna and flora), today or in the future. And in regions
65 where water stocks fluctuate over time and space, in the absence of government institutions, too much would be used by those who can access it first, and too little by those farther away or slower to reach it. Thus we live in a world where the quantity of water used by humans is limited by water rights institutions and where the prices users pay are set administratively to cover water utility costs (plus fair profits in the case of privately owned utilities). For these reasons, water prices are not freely determined competitive market prices. Furthermore because the quantities of water used at those prices are not freely determined in all sectors, a regional water price does not reflect the marginal value product of water in all economic uses in the region. In sum, we cannot infer the true underlying social values or costs of water from observed water prices. The evaluation of policies to alter water infrastructure, changes in regional water supplies or demands, inter‐basin water transfers, or changes in water quality requires the quantification of water values for comparison over alternative scenarios. Because we cannot infer the value of water from observed water prices, we must construct economic models to estimate it instead. When we construct such models, however, we must formalize that the marginal value of water used by firms in sectors where water use is subject to quota is not necessarily equal to the price paid. Furthermore, we must formalize the possibility that the transaction price does not equal the social cost of water in the region. We can, however, infer that the price paid by unconstrained sectors does equal the marginal value product of water for the quantities they actually use. As an input to production,
66 the value of water is a function of its productivity and the price of the output. And firms will use the amount of water at which the price they pay for the water just covers the value of output it produces. 3.1.2 Determining Water Prices among Regions and over Time Due to the imperfect mobility of water across space (as well as time), even a free market water price (gross of storage and/or conveyance costs) will vary from location to location and over time. These facts are demonstrated in Australia’s Watermove market. In Australia, water property rights were separated from rights to land in 1994 in a bundle of changes called the Council of Australian Government’s reforms (COAG). The COAG reform also established a fairly well‐structured water market (Schreider, 2009). We can obtain some important insights about the determination and variability of water prices from observing their experiences. Amongst water users within a single defined trading zone, temporary and permanent water trades take place through a water exchange called Watermove (Schreider, 2009). Trading zones are defined by the infrastructure and topography that make a physical water trade possible. Within zones, and using actual bids from buyers and sellers, Watermove determines a single price for short term water trades on a weekly basis. Schreider (2009) showed how time and regional weather in each zone is reflected in these water prices by examining the prices for a Temporary Water Right/Diversion License in 1A Greater Goulburn Victoria in a relatively wet season (2005/06) and in a drought year (2006/07). In the wet year, the weekly price of a megaliter of water in this zone varied from 12 to 80 AUD. In the dry year, weekly prices
67 for the zone ranged from 300 to 950 AUD. Schreider describes these prices as being characterized by upward and downward jumps, with a level specific to a particular season and a drop towards the end of each season. In regression analysis of another set of Watermove regional (zonal) water prices, Wittwer and Griffin (2012) show that water prices are negatively related to the volume of water available to trade that year, positively related to a drought index, and positively related to farm output prices. Over the ten‐year period the average price of a megaliter of water in Goulburn basin varied sixteen‐fold, from 35 to 562 AUD. These examples illustrate the effects that variations in water availability and water demand over time can have on water values and prices. There is no single, constant value or price for water. While the overall supply within a region where water can feasibly be traded is a primary influence on the value of water in the region, Wittwer (2012) reports that the price of water in production also depends on:
prices and availability of all other inputs of production, such as wages,
investments made in multi‐period capital, such as perennial crops or livestock
substitution possibilities available for water
prices of trade goods from other regions
opportunity costs (the prices paid for water use by other industries and for consumption by households).
68 Finally, direct observation of prices for trades in long‐term water rights, just like land and housing prices, have been shown to be subject to boom and bust cycles (Young, 2005). The Watermove market prices can be used to infer the marginal value of water as an input across uses within individual regions. This innovative program and the resulting markets illustrate the fluctuations that occur in water values over time and by use and region. Suppose that reductions in the availability of water or growth in the demand for water in a given region create enough of a differential in prices across regions to justify an investment to convey the water between the two regions. The ultimate equilibrium prices of water would differ only by the marginal cost of conveyance once the two regions water markets are integrated. The net gains from interregional water trade would be the sum of the net gains in the many sectors that would be affected in each region, both through primary effects as the industries and consumers respond to the change in water price in their region, and through the secondary effects due to the changes in income and spending work their way through the interdependent agents of the regional economies. In the case where water prices reflect the marginal value of water, a computable general equilibrium modeling approach would be simpler to specify and clearly appropriate for simulating the effects of and net gains from opening trade in water between regions. Unfortunately, in most regions in the US, such markets in water do not
69 exist.17 Substantial effort will be needed to properly account for the differences between estimated true social values and observed water prices, and special formalization of the institutional mechanisms that set water prices will be required to construct a CGE model. 3.1.3 CGE Model Basics CGE models are abstractions representing entire economies. One way to describe a CGE model is to consider the circular flow of money through an economy. Figure 1 presents this circular flow, adapted from Ghadimi (2007). First, start with the producers. A typical CGE model distinguishes multiple producing sectors such as the agricultural sector, the manufacturing sector, a service sector and a utilities sector. The sectors to be distinguished depend on the issue to be analyzed. Water CGE models often disaggregate the water utility from other sectors because of its key role capturing, storing, treating and delivering water to users. Production of output from each sector is formalized according to a specific (constant returns to scale) functional form chosen by the modeler, for example, a constant elasticity of substitution function. As Sue Wing (2011) notes, three neoclassical concepts are at the core of a typical CGE model. 1. Freedom of entry zero economic profits; plus no idle cash balances held by firms. All after‐tax firm revenue is spent. Net revenue from production is used to 17
Water markets for short term water trades exist in Colorado and California but are less well developed than the Australian water market.
70 purchase intermediate inputs, distributed to the owners of the primary factors of production (households), and/or spent on capital investments. 2. Perfect good and factor mobility markets clear and prices paid equal prices received (net of taxes or subsidies). Costly mobility services are intermediate goods provided by trade and service sector firms. 3. Local nonsatiation no idle cash balances held by households. To maximize
utility, households spend all their (after tax) income on goods and services, and saving. Figure 3‐1. Circular Flow of Income in a Water CGE
71 CGE models usually also distinguish a government sector, formalize the rest of the world’s demand for exports and supplies of imports, and institutions to track savings and investment. Governments collect taxes, purchase goods and services in the provision of public goods, and provide transfers. In some water CGEs, the government sector sets and collects water ‘prices’ or fees. The specification of savings behavior and investment is relevant in dynamic CGE models for policy questions about water supply infrastructure over time. Specification of trade with other regions is a standard in CGE models, and it is also important for assessing the implications of opening up water markets. 3.1.4 Partial versus General Equilibrium Approaches to Valuing Water in Economies Partial equilibrium approaches hold other prices and marketed quantities constant, while focusing on a specific water use. Partial equilibrium methods include the hedonic property value method, stated and revealed preference, non‐market valuation of the value of water to recreation and ecosystem services, estimation of production functions or demand functions, the residual method (subtracting all other input costs from total revenue), and linear programming input‐output models where prices are fixed (Young 2005). Partial equilibrium analysis may be appropriate for analyzing small changes in water attributes (supply, quality, timing, flow, or price) that are unlikely to affect the prices of other goods or services throughout the economy in an appreciable manner. However, for non‐marginal changes in water supplied or pricing associated with many types of water policies, the direct and secondary influences on other commodity
72 and factor markets may be of consequence throughout an economy. Because partial equilibrium approaches cannot account for secondary effects, estimates of changes in water demand and prices from partial equilibrium analyses could lead to over or underestimates of changes in water values, depending on the extent and type of linkages in the regional economy affected. CGE models represent all the interrelationships among markets and sectors in regional economies. All prices and market quantities are endogenous. CGE models are appropriately applied where water pricing and supply can affect multiple markets and sectors in non‐marginal ways. CGE models can be used to simulate the cumulative impacts of changes that affect any one (or combination of) sectors, to predict changes that will result throughout the entire regional economy. The literature includes many examples of CGE models used to examine the economic consequences of alternative water projects, allocations, or prices, as well as the effects of increasing water scarcity. The existing literature on water‐CGE models gives examples of the types of general equilibrium effects that cannot be accounted for in partial equilibrium methods. A good example of the secondary effects accounted for by CGE models is provided by Hassan and Thurlow (2011). They apply a multi‐regional CGE model of South Africa to compare water trade liberalization policies. They find that creating a water market amongst rural farmers improves the welfare of rural farmers but hurts the urban poor because the prices of cereals increase when the price of irrigation water increases, encouraging farmers to grow higher value vegetable and fruit crops rather than grains. In this example, higher water prices lead to different crop
73 mixes, price changes for agricultural commodities, and different income effects for urban and rural poor. \ 3.1.5 When to Use a Water CGE Model The types of economic problems concerning the value of water resources that are best modeled using a CGE approach have the following attributes: (1) the value of water as an input to one or more industrial sectors in a well‐defined regional economy is a relatively high proportion of the total value of the output of those sectors (2) sectors (mainly agriculture) buy inputs from or sell their outputs to other sectors in the economy, such that changes in the water‐using sectors are likely to affect value‐added in other sectors, (3) the boundaries of the regional economy to be modeled are well defined in terms of water use, such as a hydrological basin, a watershed, a water utility district, or rivershed, (4) there is sufficient demand for the simulations provided by the water‐CGE model to justify the relatively large investment needed to design, parameterize, calibrate, and validate the model. 3.1.6 Challenges Posed by CGE Models in General Unlike some types of econometric work, CGE models are not constructed to test hypotheses about producer or consumer behavior or the structure of an economy, to estimate the validity of a functional form, or to estimate parameters. A CGE modeler must make assumptions about the behavior of producers and consumers, the structure of the economy, and the functional forms to represent those structural assumptions. They must also calibrate some coefficients to replicate the baseline data (see also Gillig and McCarl 2002). A CGE model is not used to test hypothetical model specifications,
74 rather it is the specification. The results from a CGE model reflect all the assumptions used to build it. If CGE model results confirm the implications of neoclassical economic assumptions, it is because the modeler built the CGE to be a neoclassical economy, not a confirmation that the real world is a neoclassical economy. Because of their size and complexity, CGE models in general have a reputation for being a “black box” (Sue Wing, 2011). As discussed previously, the assumptions required to specify a CGE model determine the simulation findings. Not all modelers are knowledgeable enough about the phenomena they are attempting to model, or know how to formalize those phenomena, to construct or interpret valid CGE analyses. A useful CGE approach takes more time, care and skill to specify and interpret than many partial equilibrium approaches. 3.2
Water‐CGE Literature Review A review of both published and unpublished papers describing and applying all
types of water CGE models was conducted by searching three databases: Econlit, Web of Knowledge and Google Scholar. Publications on this topic have multiplied in recent years. From 1990 to 1997 I found only eight papers, compared to 38 papers between 2008 and 2012 (Figure 4). The application of CGE models to the economics of limiting greenhouse gas emissions may be increasing the technical skills and data availability for other types of environmental CGE models, including those that concern water. ‘Water‐CGEs’ differ from other CGE models because water‐CGEs either include a satellite account for water or, in a few cases, focus on a water‐related issue, such as drought, with a clever specification that avoids the need for a satellite account. At
75 Figure 3‐2. Water‐CGE papers by time period.
Water CGE papers ‐ all types 38
40 Number of papers
35 30 23
25 20 15 10
11 8
5 0 1990‐1997
1998‐2002
2003‐2007
2008‐2012
Time Period minimum, a water satellite account documents the amounts of water, in physical units such as acre‐feet, used by producers and consumers in the baseline scenario. The accounts also specify how much total water is available for withdrawal in the region under consideration. Sometimes much more hydrological detail is included. The following review concentrates mainly on those models published in academic literature in the English language. A large number of water‐CGE models may exist, but have not been published. As a result, not all models known to me have been included. Water CGE models have been used to address a variety of water‐related policies. Three of the most common (often interrelated) policies are:
76 1. Economic and water use impacts of water resource re‐allocation amongst agricultural sectors, often by means of liberalizing institutional barriers to water markets (e.g., Roe et al. 2005) 2. Economic and water use impacts as well as distributional effects of re‐allocating water resources between agricultural and municipal water users (e.g., Watson and Davies 2011) 3. The welfare maximizing amount of water supply infrastructure such as dams, pipelines and desalinization plants (e.g., Wittwer 2009). Other applications include measuring the economic effects of drought (Horridge et al. 2005, Wittwer and Griffith 2011), global warming (Roson et al. 2010, Qureshi et al. 2012), population increase (Watson and Davies 2011, Wittwer 2009, Qureshi et al. 2012); interaction of water trade liberalization with trade tariffs on agricultural goods (Roe et al. 2005, Robinson and Gehlhar 1995, Tsur et al. 2004); winners and losers in world trade should water supplies be restricted (Berrittella et al. 2007), priced differently (Berrittella et al. 2008), or used more productively (Calzadilla et al. 2011); analyzing trade‐offs between agricultural water use and environmental water use (Seung et al. 1997, 1998, 2000, Dixon et al. 2011, Wittwer 2011); economic effects of sudden supply disruptions (Rose et al. 2005, 2011); water tariff and tax policy analysis (Letsoalo et al. 2007, van Heerden et al. 2008); and the estimation of the value of water quality (Brouwer et al. 2008). Many, but not all of these models are used to find a price or shadow price for water in agriculture or for municipal water.
77 The great majority of water‐CGE models address agricultural water use. This is because:
Irrigation agriculture is by far the largest consumer of water extracted for human use (see Figure 3‐1 for trends in worldwide water extraction and consumption).
The share of production expenditures on water is the largest in agriculture; in comparison other sectors spend only a very small fraction of production cost outlays on water (Young 2005).
Institutional barriers and subsidies often isolate agricultural water users from other water users, and competitive water markets usually do not exist, so large water price differentials among sectors exist, making agricultural water use a subject for policy study and change.
Data on crop water use is usually available and has sometimes been studied intensively.
Data on water use for commercial and many industrial enterprises is often not available separately from municipal water use and/or is not of much interest, because commercial and industrial water use is usually insignificant compared to household use of municipal water supplies.
Lack of data and previous work on water valuation for industrial demands make other sector water use subjects more difficult (Young 2005).
78 Figure 3‐3. Time trends for world water extraction and consumption by sector.
Source: (UNEP, 2008, http://www.unep.org/dewa/vitalwater/article3.html)
Very few water CGEs attempt to estimate the marginal value product for water in industries other than agriculture. There is generally little research on the economic value of water to industry or commercial enterprises using any type of model (Young 2005). A large share of the work that has been done concerns Canada, where a survey done every five years continues to give researchers access to high quality data on water use by industry (Renzetti 2005). In any event, only in the case of self‐supplying industries
79 such as agriculture or the water utility sector is there usually any limit or constraint, so for many sectors, as discussed above, we can infer the MVPs of water in those other productive uses from the prices paid.18 A few models include separate treatment of nonagricultural industries (Gomez et al. 2004; Mukherjee 1996; Rose et al. 2005, 2007; Letsoalo et al. 2007; van Heerden 2008; Hassan and Thurlow 2008; Goodman 2000).19 Often industrial and commercial water use is considered collectively under the rubric of municipal water, for which there is often assumed to be only one market and one price. Many challenges uniquely confront water‐CGE modelers:
obtaining the appropriate data on water use by sector
obtaining hydrological data on water availability
specifying the appropriate elasticities of substitution between water and other factors in production
choosing the appropriate specification for production functions including water as a primary factor of production or as an intermediate input
finding baseline values for water or ways around that in cases where water use is not freely determined or is not available.
Many of these problems are related to the specification of the production function, which is an essential element in determining the value of water as an input.
18
Water quality issues can create significant water use constraints on utility supplied customers. Four are South African studies. South Africa apparently routinely provides more detailed data on water use, such as for industries like mining. 19
80 Below we discuss production functions in water‐CGEs, as well as some of the other important equations in CGE models. 3.2.1 Structure of Water‐CGE Models The Production Function The backbone of the water‐CGE specification is usually the production function. This formalizes how water is used to produce goods. In water CGE models, land, water or both are usually included as a factor of production. In the water‐CGE literature, water appears in production functions in three main ways: 1. As an intermediate input (for example, like fertilizer or marketing services) 2. As a factor of production (for example, like capital or labor or land) 3. Implicitly (for example, in a land factor which has differential productivities due to different levels of precipitation or irrigation water applied). Some models of municipal water demands do not include full water accounts, instead assuming “one price” for municipal water and using total municipal water use and data for purchases from the water utility sector to model water use (Wittwer, 2009). Municipal water is modeled as an intermediate input to industry and as a final demand good to households. Two other CGE models that focus on municipal water demands distinguish “indoor water” from “outdoor water” by season and periods, with peak loads included in the specification (Dixon 1990; Horridge et al. 1993). Where trade‐ offs between urban water demands and water for crop irrigation are of central concern, treated water for urban use and untreated water for irrigation usually enter the production function as an intermediate input and as a primary factor of production,
81 respectively (Watson and Davies, 2011, GTAP‐W model used by Berrittella et al., 2007, 2008). The third case of implicit inclusion of water is most often seen in water CGE models focused on agriculture. In Horridge et al. (2005), water is implicitly a factor of production. A separate model is used to calculate how varying degrees of drought change the productivity of land across regions. The varying productivity of land is then incorporated into the production functions in the water‐CGE. In Seung et al. (1997), Seung et al. (1998) and Seung et al. (2000), water is assumed to be used in a fixed ratio with land, and land is a factor of production only in agricultural sectors; thus, irrigation water use is equivalent to a given amount of land use. This specification formalizes both legal and natural resource constraints in arid Nevada, where water rights (and thus use) in the main agricultural sectors producing hay and livestock are specified per acre. In Robinson and Gehlhar (1995) and Berck et al. (1991), land and water are also used in fixed ratios. In these models, however, different agricultural sectors have different ratios of land to water use and land is mobile or partially mobile across sectors. No rain‐fed agricultural sectors are specified, limiting the extent to which water and land can be substituted for each other in production. This is in contrast to Goodman (2000), where land and water substitute for each other. The production block in water‐CGE models generally follows the typical specification in CGE models. The typical CGE model employs functional forms that specify substitutability between the primary factors of production: labor, capital, land (and sometimes water), and fixed proportions of intermediate inputs (including water).
82 Indeed, a Leontief (fixed proportion) relationship of water input to output is often assumed, usually in a nested structure. Thus non‐water primary factors substitute for each other, but nothing substitutes for water. Cobb‐Douglas or constant elasticity of substitution (CES) functional forms are the most popular. To allow for different degrees of substitutability between different factors, multi‐level nests are used. Most water‐CGE models use a variant of these typical specifications (see Figure 3‐2): 1. Imports and extra‐regionally sourced domestic alternatives are modeled as imperfect substitutes, according to a CES function (the Armington Assumption). Producers choose the mix and level of imported and other domestic items to satisfy their demands for composite non‐local intermediate inputs. 2.
Non‐local (imported and other domestic) intermediates are modeled as imperfect substitutes for the locally‐produced versions, also according to a CES function (and the Armington Assumption). Producers choose the mix and level of local and non‐local composites to satisfy their demands for intermediate inputs.
3.
Composite intermediate inputs are demanded, usually in fixed proportions (Leontief Assumption), with respect to output.
4.
The intermediate inputs are combined with primary factors, specified in most water‐CGE models as a Cobb‐Douglas or CES composite of labor, capita, land and water. Several levels of nesting may also be used for primary factors as well. Some water‐CGE models focus on agriculture to such an extent that water use
for urban purposes is not specified in the model. For models concerning urban water use, or urban and agricultural water use trades, a water utility sector is typically
83 Figure 3‐4. Sample of Production Technology
formalized. The water utility sector uses water as a factor of production to produce treated water. Producers who cannot self‐supply water then purchase treated water and perhaps sewer services from the water utility, as an intermediate input. In some models, households also purchase the treated water and sewer services as a final good or consumer commodity. Private and Government Demands Water‐CGE articles focus more often on producing sectors than on private demand. To formalize private demand for water, a generalized Cobb‐Douglas (Stone‐ Geary) utility function was most often used to represent the utility of consumers, leading to a linear expenditure system. Goodman (2000) specifies a utility function that
84 directly includes water, as does Dixon (1990). A few models include and track in a satellite account the water used by consumers. Consumers are also assumed to treat imports and local goods as imperfect substitutes (the Armington Assumption again). Government and investor demands for final goods rarely receive special attention in water‐CGE models. Thus the formalization may be quite crude. Some modelers do not separately specify a government sector (Goodman 2000). Gomez et al. (2004) model the government as a passive recipient of taxes which it immediately transfers to households. In that case the sectoral mix of goods in government final demand is implicitly exactly the same as the mix in household consumption. Some modelers, (e.g., Robinson and Gehlhar 1995) specify government and investor final goods consumption exogenously. Many (e.g., Horridge et al. 2005), assume that the mix is exogenous according to government and investment spending shares, while levels are endogenous to revenues or saving; as in a typical CGE model. Naturally, government revenues are important where taxes, subsidies or tariffs are being analyzed. Roe et al. (2005) and Tsur et al. (2004) model how macroeconomic trade reforms in Morocco interact with water market reform. Robinson and Gehlhar (1995) also model the effects of removing sectoral subsidies, taxes and tariffs in Egypt in the context of changes in water policy.20 Government also can be an important piece of the technical specification of the water sector or factor. Water ‘taxes’ may be collected and redistributed to account for the public good treatment of water. For example, 20
Roe et al. (2005) and Tsur et al. (2004) are two of a suite of papers and book chapters related to the same Moroccan water‐CGE issues. Other publications on the issue include Goldin and Roland‐Holst (1995), Diao and Roe (2000, 2003, 2005).
85 Berrittella et al. (2007) and Letsoalo (2007) both model the water sector with administratively set prices as a non‐market sector. Taxes are distributed back to households. Where the timing of investments in large infrastructure projects, such as dams, is the subject to be analyzed, significant attention is turned on to the investment sector. For example, Dixon (1990) and more recently Qureshi et al. (2012) carefully model investment dynamically to analyze water projects such as dams and water treatment plants. Factor Markets, including Land and Water Wages, rents and returns to other primary factor suppliers are determined endogenously in CGE models, unless an institutional feature requires otherwise. The simultaneous solution of producing sectors’ demands for factors such as water and land and households’ supplies of these factors determines each factor’s market‐clearing price. When factor supply is assumed to be exogenous, the equation appears in the ‘closure’ section. Changes in factor supplies across regions or factor employment among sectors are the most important determinants of economic outcomes. Accordingly, the specification of primary factor supply or mobility is very influential in CGE simulations. By the same token, primary factor mobility and substitutability are critical determinants of the productive value of water. Water‐CGE models are the same as most CGE models with respect to distinguishing labor and capital as primary factors of production. The degrees of inter‐ regional and inter‐sectoral mobility of primary factors of production are important
86 modeling decisions. When the focus is on water trading between urban and rural groups, assumptions regarding regional labor mobility are important. To formalize that farm workers would leave the area if agricultural water use is constrained, assume a high degree of interregional mobility (and low intersectoral mobility). If workers would simply change occupations but not migrate, assume high intersectoral mobility and low interregional mobility. As Seung et al. 1998 showed, in the latter case the impacts on a regional economy of water restrictions are low. If non‐irrigated agricultural activities exist and can provide alternative employment of labor, land and capital, this mobility can easily be formalized in CGE models. In agriculture‐focused water‐CGE models, water and land are typically assumed to be at least partially mobile across agricultural activities. But they are not typically mobile across regions. In one exception, Peterson et al. (2005) simulate costless water mobility across regions, to demonstrate the limiting case of pure mobility, recognizing that interregional water transfers would actually entail high costs. The physical realities in the region within which water trades among users may occur are important, especially for finding shadow values of water. Within an area that can physically and institutionally trade water between sectors the net price received by water suppliers (net of water transfer costs) will equilibrate, but the values of the marginal products of water in each sector will differ according to the costs of water delivery to each customer. Sectors between which water cannot move should be modeled as separate markets, where distinct prices can or will prevail, even in competitive free market
87 equilibrium. To model the effects of water trade between rural and urban activities, Gomez et al. (2004) initially assume that water is not mobile between the agricultural sectors and the drinking water supply sector. Then they relax this to allow trade, comparing the resulting simulated economy to the initial economy. Goodman (2000), also concerned with water trade between rural and urban uses, also does not initially allow water mobility between agricultural and to municipal uses. In Goodman (2000) water is a factor of production in all sectors of the economy, including non‐agricultural sectors, and raw water directly enters the consumption function as a consumer good. One counterfactual scenario assumes complete mobility. Finally, institutional barriers to movements of goods trade can also be significant determinants of water use and thus value. Product Markets for Water In CGE models prices endogenously equate supply and demand in a Walrasian general equilibrium framework. Unless otherwise specified, markets are assumed to be perfectly competitive. As noted, it is often the case that observed water prices reflect government policies rather more than the workings of the marketplace. Government‐ administered water rights ensure that specified amounts of water are available to agricultural sectors at low prices. The shadow price cannot be expected to equal the price paid for water in agriculture. Several water‐CGE models assume that water rights do not bind and the initial price is zero. Implementing a binding constraint leads to a non‐zero shadow price of water. For example, the water CGEs using GTAP‐W (Berrittella et al. 2006, 2007, 2008; Roson 2010) and Gomez et al. (2004) assume that baseline
88 water supply exceeds demand (at all prices), so that price is zero in the baseline. Simulating reductions in water supply gives rise to a market for water. Then water’s MVP is shown as the market clearing price. In the GTAP‐W models an industry‐specific water price elasticity is formalized, so that as the price of water rises, water use intensity changes. The assumed elasticity is presumably due to flexible efficiency in water use; it is not explicitly due to substitution for water by other factors of production. Other water CGEs specify a market for water as a factor of production in the baseline model whether or not there is an existing market in the economy being modeled. To do this the baseline value of water by sector must be found and netted out of the gross operating surpluses documented in the Social Accounting Matrix (SAM) used to parameterize the CGE model. Sales prices for water rights can be annualized for this purpose (Young, 2005, Wittwer, 2012). Sometimes the difference in productivity between dryland and irrigated land is used to proxy water rents (Young, 2005, Wittwer, 2012). Where a short‐term water market exists, the average lease price has been used (Goodman 2000, Watson and Davies 2011). We observe a variety of methods for tackling these difficulties:
Estimation of water ‘rent’ in various ways, often using land values. The rent is then subtracted from gross operating surplus and distributed to households. This technique assumes a functioning long term water factor market (for example,
89 Robinson and Gehlhar, 1995, TERM‐H2O models described in Wittwer 2012, Seung et al. 1997, etc.).
Assumption that no market for water exists in the baseline, that water is in surplus and its price in equilibrium is zero, becoming positive only as water supplies are withdrawn (Examples are Berrittella et al. 2007, 2008, Diao and Roe 2005, Roson et al. 2010).
In one case, water ‘rent’ is subtracted from utility fees charged to industries (Hassan and Thurlow 2011).
Some modelers use administratively‐set utility fees for treated water as if they were determined by a market equilibrium. Other water CGE modelers are not concerned about the market value or shadow
price of water, but are concerned about the effects of changes in administratively set prices. Letsoalo et al. (2007) are primarily interested in changing water tariffs to obtain a “triple dividend” of reduced water use, reduced tax distortions on the rest of the economy, and increase in income for poor households. In Letsoalo et al. (2007) the price of water is a tariff imposed by the government, and water is a non‐market commodity. 3.2.2 Applications of Water CGE Models Water Demand Curves Berck et al. (1991), Gomez et al. (2004), Mukherjee (1996), Robinson and Gehlhar (1995) and Tsur et al. (2004) all use their CGE model to map out demand curves for water. The technique used is to constrain water availability in steps (e.g., 95 percent
90 of base, 90 percent of base, 85 percent of base and so on), recording the resulting shadow price of water. Where free markets exist, these will equal the market prices. Mukherjee (1996) constructs a demand curve in terms of market prices. Tsur et al. (2004) and Berck et al. (1991) find the demand curve implied by the set of shadow prices. Robinson and Gehlhar (1995) and Gomez et al. (2004) trace out various shadow prices as well as market prices. Efficient Water Use in Agricultural Sectors. Many water‐CGE articles (Robinson and Gehlhar 1995; Tsur et al. 2004; Berrittella et al. 2005; Horridge et al. 2005; Peterson et al. 2005; Roe et al. 2005, Roson et al. 2010, Wittwer 2011, Wittwer and Griffith 2012) focus on policy and water use in the agricultural sector. Water CGE models are used to experiment with different government policies thought to bring about a more rational and efficient use of water. In their studies of Morocco, for example, Tsur et al. (2004) and Roe et al. (2005) investigate the effects of trade reform at the macro level on agricultural water use, as well as instigating water trading or other water policy reforms at the farm level. They find that if water market reforms occur before macro level trade reforms, inefficiencies in water use will increase with the market reform. Peterson et al. (2005) explore the ramifications of introducing water trading amongst agricultural sectors in Australia under conditions of decreased water availability. Decreasing water availability reduces gross regional product. But if either inter‐sectoral or inter‐regional trades of irrigation water are allowed, the additional flexibility in the system mitigates the negative impacts of decreased water availability.
91 Berrittella et al. (2007), and Roson et al. (2010) note that implicitly, water trades take place whenever agricultural goods are traded, due to the virtual water embedded in the traded products. They adapt the Purdue University GTAP multi‐regional CGE model of the world economy to track virtual water trade under conditions of water scarcity, finding that where agricultural subsidies have distorted food production, the new water constraints in some cases actually improved welfare. Trade‐offs between Urban and Agricultural Water Use As urban water demands for household and industry use increase, they compete with agriculture for scarce water resources. The price paid by farmers for irrigation water often does not reflect the opportunity cost of water, which is the value of the water in the best alternative use. Often water shadow prices for urban use are many times the shadow price of water used in agriculture. Berck et al. (1991) provide an example: in the San Joaquin Valley in California at that time, the price paid for irrigation water from federal projects was $20 to $30 per acre‐foot, while the value in agricultural production was estimated to be $60 to $70, and the value of the “next unit sold to the highest bidder” was somewhere between $1,000 and $2,000 in nearby urban areas. Berck et al concluded that water could feasibly be purchased from farmer’s to reduce the salinization problems of the region. However, the large differential in water MVPs can also indicate that food prices are too low because of distortions in the agricultural markets – that we do not pay enough for the water embodied in food. One way to allocate water to its highest market–valued uses is to create a water market between agricultural firms and municipal water utilities. To simulate the effect
92 of creating a water market, water‐CGE models that have been parameterized assuming perfect competition in all but the initial water market are used to simulate a perfectly competitive water market counterfactual. Overall economic performance in such counterfactuals is always found to be superior to the initial situations. However, as with any change, there are winners and losers, which the model can help identify. These models, however, generally assume zero water losses when moving water from one user to another. In reality, transmission losses can be quite high and should be modeled where data is available. Urban Water Use Of the five CGE models in the literature that focus solely on urban water use, two find the ideal price for an urban utility to charge (Dixon 1990, Horridge 1993), two examine short run impacts from earthquake‐disrupted water supplies (Rose and Liao 2005, Rose et al. 2011), and another explores the circumstances under which the infrastructure investments for municipal water demand would have been optimal (Wittwer, 2009). Dixon (1990) develops a water‐CGE model to find the appropriate price for water utilities to charge their customers, a task usually accomplished with a partial equilibrium model. Horridge et al. (1993) explore the same question with a more sophisticated approach that includes stochastic water supplies. The shadow price of water is used to guide decisions about how much water supply infrastructure to build and when. Wittwer (2009) uses the multi‐regional dynamic form of TERM, the Australian regional CGE model, to evaluate the need for a dam or other supply infrastructure projects in SE Queensland.
93 3.2.3 Special Challenges for Water CGE Models Dynamic CGE models All CGE models implicitly incorporate time, in that adjustments occur until markets clear (Ghadimi, 2007). Models may be designed to represent short‐run changes, in which capital or other factors are not mobile between sectors or regions, or long‐run changes that assume full mobility. Many water‐CGE models incorporate a more explicit time element in order to observe the temporal effects of a policy adjustment. Many water resource policies have consequences that unfold over multiple time periods, or questions related to capital stock accumulation. One such important water policy issue is to determine the appropriate amount of investment in supply infrastructure such as dams, pipelines, or desalinization plants. This is an investment and capital stock decision that relates to the economic value of the water that is to be moved and supplied over time. Thus water‐CGE models that focus in part or in total on infrastructure supply generally also incorporate time. Examples include:
Dixon (1990) determines the optimal price for an urban water authority in Melbourne, Australia. To do this, he considers the appropriate level of investment in, as well as timing for, infrastructure related to water capture, storage, treatment, delivery and sewer service as the population increases (Dixon 1990).
Goodman (2000) compares the installation of a new dam to meet urban demand, which grows with population over time, to temporary market trades of
94 water between agricultural sectors and municipal water suppliers. Consumers in this model maximize utility over time, while precipitation is allowed to vary from year to year (Goodman, 2000).
Wittwer (2009) also conducts an analysis of the appropriate level of supply infrastructure to meet urban water demand in Queensland, Australia. In the TERM‐DYN model interregional wage differences cause labor migration over several time periods. Labor supplies and population may therefore change over time depending on the relative economic conditions between regions. This affects the optimal supply of water infrastructure. Precipitation levels may be specified to rise or fall with time, while technical change can increase water use efficiency over time. Capital and labor used to build infrastructure is withdrawn from other activities over several time periods (Wittwer 2009).
Other water policy issues that involve multi‐period implications include drought, because of its long‐term implications for livestock herds and for perennial crops such as fruit and nut trees (Wittwer and Griffith 2011); climate change scenarios that play out over decades; or policy changes that take time to institute, such as buybacks of water rights for environmental purposes (Seung et al. 2000, Wittwer 2011).
Ghadimi (2007) distinguishes two basic types of dynamic CGE models: 1. recursive models that solve for a static equilibrium, update time‐related variables and solve for the next time period equilibrium in sequence for the required number of time steps, and
95 2. models that incorporate inter‐temporal optimizing behaviors based on expectations. Most water CGEs use the recursive technique. Dixon (1990) and Goodman (2000), however, specify consumers who solve an inter‐temporal utility optimization problem. Dynamic models such as TERM‐H2O use three different types of adjustments to update variables to a new time period: capital and financial asset/liability accumulation, and lagged adjustments to factor supplies.21 Changes in factor supplies, such as the effect of population growth on the supply of labor, may be imposed exogenously. Changes in water supplies may also be imposed exogenously over a period of time. A baseline for a dynamic model is usually constructed using forecasts of economic growth, population growth, and/or water availability.22 This baseline model will then be compared with the same model subjected to policy or other exogenous shocks. Water Accounts Data Building a standard CGE model is an extremely data intensive enterprise, requiring detailed baseline data for all parts of the economy. Adding the ability to address changes in water resources requires additional data about the water used by economic sectors. Wittwer (2012) refers to this additional data as “water accounts.” Finding sources for water accounts data can be a challenge. The water‐CGE modeler usually needs estimates of water use by industry sector. Depending on the model
21 22
P.38 Ibid. P.45 Ibid.
96 purposes, water use by households and government, total water availability in the region, and other more detailed water data may also be needed. The most water‐intensive sectors are usually agricultural. Typically the modeler collects data on current water use or tries to estimate the usage according to acreage planted, yields, and crop water intensity factors. Mubako (2011) estimated county‐level water withdrawals and consumption for some agricultural sectors. While one can estimate agricultural water use from various sources of crop production data, detailed sectoral water use data for other types of industries are less available in the United States. For many sectors, water is supplied by utilities which do not typically track water use by detailed industry sector. Water use data about self‐supplying industries such as certain types of manufacturing and mining sectors is also not easy to find. In the United States, another heavy water user is the electricity generating sector, although on average a large amount of the water withdrawn for cooling is returned to the same region. Data on electricity sector water withdrawals is available from various sources. A majority of countries included in a 2009 U.N. survey on the topic either have or are planning to collect data for water accounts (Wittwer 2012). The United States is not one of these countries. Estimates at a very aggregated level are available from the United States Geological Survey. Blackhurst et al. (2010) used USGS data to estimate water intensity factors for each BEA industry sector (Blackhurst et al. 2010a, 2010b). Fadali extended this estimation to the state level (Fadali 2012) and to county level for the application in this essay.
97 The availability of more detailed water accounts data is major reason why there are so many water‐CGE models of Australia, Morocco, and South Africa.23 Unfortunately, there is a lack of water accounts data in the United States. The existing published U.S. water‐CGE models are constructed from unique datasets about water resources, allocation and usage in the particular regions. Berck et al. and Seung et al. consider only agricultural water use (Berck et al. 1991; Seung et al. 1998; Seung et al. 1997; Seung et al. 2000; Seung et al. 1998; Seung 1999; Seung et al. 1997). Water resource and allocation data was derived from the California Dept. of Water Resources in the case of the Berck et al. study, and from related federally‐sponsored environmental impact studies in the case of Seung et al. Goodman, and Watson and Davies, who model municipal as well as agricultural water users (Goodman, 2000; Watson and Davies 2011) prepare their water accounts from data provided by the Colorado Division of Water Resources, a state research institute, the USGS, and a local water board. The two papers on sudden disruption of water supply concern only municipal water supplied by a utility sector and use only IMPLAN estimated purchases of water utility commodity (Rose and Liao, 2005, Rose et al. 2011). Sector (dis)Aggregation for Water CGE Models Because of the complexity and size of the non‐linear system involved, a CGE modeler leaves as many industry sectors aggregated as possible, while avoiding 23
Australian water CGE studies include Dixon, Rimmer and Wittwer (2011), Dixon (1990), Horridge, Dixon and Rimmer (1993), Horridge, Madden and Wittwer (2005), Peterson et al (2005), Qureshi et al (2012), Wittwer and Griffith (2012), Wittwer (2011; 2009; 2006). South African water CGE studies include Hassan and Thurlow (2011), Hassan et al (2008), Letsoalo et al (2007), Mukherjee (1996), van Heerden, Blignaut and Horridge (2008). Moroccan studies include Diao and Roe (2000a; 2000b), Diao, Roe and Doukkali (2005), Hassan and Thurlow (2011), Hassan et al (2008), Tsur et al (2004).
98 aggregation error as much as possible. But water demand may vary widely even within a detailed disaggregation of industry sectors. To minimize aggregation error, sector disaggregation is customized for the region of interest. Large water users who behave distinctly should be distinguished. For example, in input‐output data such as that provided by IMPLAN or RIMS from the Bureau of Economic Analysis, ranches and feedlots are usually aggregated into one sector. But Watson and Davies (2011) split this sector into two in their model of northeastern Colorado, because “these two operations use land and water in very different ways and interface differently with the regional economy” (Watson and Davies 2011, p. 346). Because of the relative magnitude of water used in agriculture it is important to model agricultural water use in more detail. Agricultural activities that receive distinct output prices or that have distinct marginal physical products of water should be disaggregated. For example, the three largest agricultural outputs in Nevada are hay, beef cattle and dairy (National Agricultural Statistics Service 2009). Each activity has a distinct production technology, distinct water intensity factors, and faces distinct sources of market demand. Furthermore, because Nevada’s water right institutions inspire use‐it‐or‐lose‐it behavior, the levels of water used are not determined to equate water’s MVP to the water prices paid (if any). Thus it is appropriate to disaggregate these agricultural sectors. In cases where the economic impact of a water policy is suspected to have differential effects on different types of households (i.e., rural or urban, low income or high income), the household sector will also be disaggregated. Watson and Davies
99 (2011) distinguish rural from urban households. Rural households own water rights and are compensated for their water rights by tax payments from urban households in the water trade simulations. Rural households earn the returns to their labor and capital employed in the agricultural sectors, and as such lose income when agricultural activity is constrained by water availability. Such a model has some of the advantages of a multi‐ regional model without requiring a full set of trade flows. Defining the region in a water CGE model In defining the regional boundaries for a CGE model focused on water, the key is the area within which water resources can be feasibly traded. To illustrate this, observe how trading zones are defined for Watermove, the Australian water market exchange (Schreider 2009). Trading zones are the areas within which the infrastructure and topography make a physical water transfer possible. A region might be a hydrological basin or basins connected by pipelines. Within the zones, a single price for short term water is determined in the Watermove exchange. If the objective of a water‐CGE model is to estimate the free market value of water in industry, the regional scope of the model should be determined by water‐relevant topography and water infrastructure, the same way that Australia identified Watermove water‐trading zones. Water markets not within connected basins are segmented. There is no reason to expect the marginal value of water in to be the same across markets that are not able to trade water.
100 The transport of water, however, from one basin or watershed to another may be the focus of policy analysis. For example, California governor Jerry Brown has announced plans to build two new pipelines to increase the amount of water exported from northern to central and southern California (Green 2012). Changes in the differentials between marginal values of water between the two regions arising from increased demands in the north, technological innovations that reduce the cost of conveyance to the south, or a subsidy that reduces the fixed cost of the infrastructure will change the incentives to trade water. When the relative marginal cost of imported water declines sufficiently, water optimally flows “uphill,” from the region where its value is lower to the region where its value is higher. For example, Qureshi et al.(2012) examines how water trading with rural Australia impacts water price within rural and urban economies. They find under global warming scenarios with lower water supplies that the shadow price of water in Melbourne would increase eleven fold without water trading with rural areas, while some rural areas’ water shadow values would be half what they would be under the trading scenario. The water CGE models in the literature include multi‐regional, segmented or separate water markets. The best example is the Australian model TERM‐H2O. In one application, TERM‐H2O was applied to isolate the effects of a water buyback program carried out for environmental reasons from the effects of a prolonged drought in Australia’s Murray‐Darling Basin (Wittwer and Griffith, 2011). Wittwer and Griffith concluded that the drought was responsible for most of the unemployment associated with the reduction of water. The multi‐regional model allows both for the trade of water
101 and the movement of farm labor across regions. It also accounts for the downstream effects on food processing sectors in each of the regions. The only published water‐CGE articles that model a single economic region larger than a watershed are based on the GTAP‐W national‐level data or focus on a policy where finding the value of water is not necessary for the analysis (Letsoalo et al. 2007; Berrittella et al. 2007; Berrittella et al. 2008; Calzadilla et al. 2010, 2011; and Roson and Sartori 2010). 24 Except for Letsoalo et al. all these papers investigate the ‘virtual water’ embodied in trade flows between countries. They show how national welfare, water use, and the balance of trade could change under scenarios of increased water prices, increased irrigation efficiency, or situations of water scarcity. Letsoalo et al. focus on which type of national water tax will bring about a “triple dividend”: reduced water use, reduced tax distortion by using water tax revenues in place of other tax revenues, and an increase in incomes in low income households. Their analysis must be at a national level and does not directly concern the value of water. I am not aware of any U.S. multi‐regional water CGE models.25 The necessary trade flow data amongst regions inside a country is rare, and it is the case in the United States. Considerable effort has been directed at making reasonable estimates of these flows for use with inter‐regional models. For the U.S., IMPLAN now routinely includes 24
GTAP‐W is a variant of the Global Trade Analysis Project world CGE model of Purdue University especially adapted to include water use at the country level. It includes water accounts for agricultural sectors only. 25 However, the originators of TERM‐H2O have created a multi‐regional CGE model of the U.S. with all fifty states that does not contain water accounts (Dixon, Rimmer and Wittwer, 2012). See "Usage‐R51, a State‐Level Multi‐Regional CGE Model of the US Economy," GTAP, www.gtap.agecon.purdue.edu/resources/download/5933.pdf.
102 estimated trade flows between regions based on commodity trade flow data from the Bureau of Transportation Statistics and the U.S. Census Bureau, travel cost data from Oak Ridge National Labs, and a doubly constrained gravity model in conjunction with IMPLAN estimates of local commodity supply and demand (Minnesota IMPLAN Group, 2009). Adding up constraints ensure that the gravity model gives back “known” results for regional demand and supply, and add up to the nationwide supplies and demands (Lindall et al., 2006). For Australia, TERM H2O modelers also use a gravity model approach to estimate trade flows between regions (Wittwer, 2012). There is high demand from jurisdiction legislators and local officials for estimates of the local impacts of water policy shocks. Multi‐regional CGE models offer these capabilities.26 Parameterization and Solution There are two types of parameters (exogenous magnitudes) in CGE models: initial period or “baseline” levels of observed prices and quantities of goods, factors, and monetary flows, and the coefficients in the functional forms assumed to specify producer, consumer, and institutional behavior. Some of those observed prices and quantities measure endowments or other levels that will not change over time. These will remain parametric, exogenous, or “fixed.” Other parameters are derived from the assumption that the baseline SAM represents the economy at an equilibrium point, depending on the functional forms chosen for production functions and the utility function. These CGE coefficient parameters are calibrated to replicate the observed baseline given the chosen functional forms and initial prices and quantities, whenever 26
P. 15 Ibid.
103 possible. Otherwise, CGE modelers assume parameter magnitudes from the existing literature or to reflect their a‐priori beliefs. An important parameter in water‐CGE models is the elasticity of substitution between water and other factors of production. When modeling agriculture, because water is a vital input for which there are limited substitutes, these are typically assumed to be low. Existing water CGE models with applications in the U.S. Relatively few U.S. water‐CGE models have appeared in refereed academic publications. I find eight published studies. Berck et al. (1991) published one of the first. They modeled the economic effects of limiting crop irrigation to reduce the salinization of soils in California’s Central Valley. By developing and applying a CGE model they were able to estimate the shadow price for water diverted from agricultural production in the context of water demand from the nearby urban areas. They estimated that urban water users could easily afford to compensate rural farmers for the profit they might lose (see the equation above) due to the diversion of water from their irrigated agriculture. Seung et al. (1997, 1998, 2000) developed a regional CGE model to estimate the economic consequences of water withdrawn from agriculture to benefit various environmental purposes, including recreation. They formalized agricultural water use by specifying an exogenous ratio of water per unit land. They concluded that the increased economic activity associated with the environmental benefits were not large enough to compensate for the lost agricultural activity.
104 Goodman (2000) developed a dynamic CGE model to compare the economic outcomes of building an additional dam versus allowing short term water trades between agricultural water users and municipalities in southeastern Colorado, concluding that the water trades did not impoverish rural regions and would meet urban demands more cheaply. Similarly, with a dynamic CGE model that simulated population growth and increasing water demand, Watson and Davies (2011) found that allowing short‐term water trades between agricultural sectors and municipal water providers in northeastern Colorado would affect an increase of about 8% in the price of municipal water and a 10% increase in the price of agricultural water. In contrast, population growth without a water market could cause an increase of 25% in the price of municipal water (and no increase in the price of agricultural water). Finally, Rose and Liao (2005) and Rose et al. (2011) developed CGE models to quantify the short‐term economic effects of water supply disruptions due to an earthquake in Los Angeles, California and Portland, Oregon, respectively. 3.2.4 Conclusion The number of applications of water‐CGE models has been growing in the recent decade. Given a world in which populations and income are growing, demands for both agricultural and urban water as well as the resulting pressures on environmental water use will continue to grow. Additional pressure may be added if ethanol made from thirsty crops becomes a significant fuel source. When changes in regional water resources due to global warming are added to the mix, it isn’t hard to imagine how
105 carefully water resources will need to be managed in the future. Given the large role that water plays in the economy, the CGE model, with its ability to handle system feedbacks, may become a more important tool for examining these issues. As water becomes more valuable, data on water use is likely to improve. Thus it is likely that more water‐CGE models will be developed and that the thirty‐five models in this survey are only the beginning forays. 3.3 A CGE Model of the Value of Water to Southern Nevada Industry 3.3.1 Introduction As an illustrative example, I model Southern Nevada including Clark County where Las Vegas resides. Las Vegas depends on the water covered by the Colorado River agreement. Since that agreement, which allocated 300,000 acre‐feet annually to the region, was signed in 1928, the population has grown from under 4,000 to about 2 million. Over the decades there has been much concern that this water will limit the future growth of the Southern Nevada economy as well as much concern over short‐ term supply disruptions due to drought. A CGE model is well‐suited for modeling the extent to which a fixed water allocation constrains economic activity in a region. As growth shifts water demand, the marginal value of water can rise to the point that purchases of water from regions outside of the Clark County region may become economically viable. In fact, in Nevada, consultants hired by Southern Nevada Water Authority have estimated the cost of building a pipeline to convey 84,000 AF a year to Clark County from rural White Pine and Lincoln Counties to be up to $7.3 billion (Brean, 2011, 2013). A multiregional CGE model
106 of the Southern Nevada region can be specified to investigate the effects of enabled inter‐basin water transfers on each regional economy. In contrast, a Nevada‐wide water‐CGE model that aggregates the entire state economy is not consistent with the physical boundaries and limits to water use. A statewide CGE model could feature water as an input and source of income, but it would give results that average economic values of water for Nevada as a whole, and the important policy‐relevant features of water transfers between individual regions would be lost. Las Vegas and its surrounding suburbs depend on the Colorado River for 90% of their water supply (McChesney, 2011). Because Las Vegas depends so heavily on the Colorado River, and because the river is overallocated with many powerful rivals for the water, Las Vegas and Clark County policy makers are concerned about how water shortages could impact the economy. The value of water, and how the value would change if there were to be a shortage, is of great interest to water policy planners as they consider how much water infrastructure can be justified to expand the region’s water supply. Could the marginal value of water rise enough to justify construction of a desalinization plant in California or Mexico? Could it rise high enough to justify the full costs of the proposed pipeline? What would happen to the Clark County economy in the event Nevada’s full allocation of Colorado River water were not available? The so‐called “White Report“ predicted dire consequences for the southern Nevada economy by 2006 if a lack of water disrupted the ‘growth economy’ before its ‘natural well‐rounded maturation’ occurred (Hobbs et al., 2004, White et al., 1992). It was claimed that without action to
107 add additional supplies to the current Colorado allotment from, for example, rural county groundwater supplies, full use of the Colorado River water allocation would occur in 2006 and that this would lead to an abrupt crisis. The White report predicted a 60% decline in construction employment and an eventual twenty percent decrease in employment and population as compared to ‘natural’ maturation of the economy over time. In actuality, this catastrophe has not occurred. Through investments in water recycling infrastructure by which every drop that goes down a drain is recaptured, reinjected into Lake Mead, and credited to the gross water allocation, effective programs to increase the efficiency of residential water use, and negotiations over Colorado River water, Las Vegas’ water supply did not constrain construction, despite record drought conditions on the Colorado. Instead, the population of Clark County reached the ‘natural well‐rounded maturation’ population level of 1.9 million predicted for 2035 in the White report by about 2006, with its growing construction sector intact.27 Nevertheless, water applied to residential landscapes does not enter any drain and thus cannot be recycled. Thus continued population growth in Las Vegas (Table 3‐1) 27
The irony was that a very dire crisis did occur in 2007‐08 when the housing bubble burst nationwide, hitting Las Vegas especially hard, as predicted in the later 2004 report, because of its ‘growth’ economy, i.e. an economy dependent on construction jobs. An additional irony was that the Hobbs et al. report, designed as an update of the White report modeled the consequences of a disruption in the economy due to a 60% decrease in the construction sector for any reason, thereby relatively accurately predicting the consequences of the Great Recession in Clark County. At it’s worst, the recession caused a 59% drop in construction sector employment and a 13% drop in total employment. Seven years after the peak construction sector activity in 2006, sector employment is down 66% from it’s peak, while total employment is down about 9.3% from 2006 levels.
108 and in the rest of the Colorado River basin will continue to reduce the excess supply of water. Competition from California, Arizona, Mexico and the other four Colorado River compact states for the river’s water and the drought in the recent decade has raised fears for the future. I will model such a water shortage using a water CGE, finding the values of water in production by industry sector. The model also finds the values of water in production by industry sector in the absence of a water market. The value differentials suggest the amount of money that could be available to fund a pipeline. Table 3‐1. Clark County Public Water Supply Year
Population
1985 1990 1995 2000 2005
572,140 741,460 992,590 1,375,760 1,710,551
Public water supply plus domestic wells (AF) 211,214 301,677 382,675 526,098 625,903
Per capita water use (AF/person) 0.369 0.407 0.386 0.382 0.366
Change 10.2% ‐5.2% ‐0.8% ‐4.3%
Source: USGS 2010, USGS Historical Water Use Data
Below I present results of modeling a 10% water shortage in the southern Nevada region. The model and data are described briefly with emphasis on where they differ from the State of Nevada model described elsewhere in this thesis or where new information can be added relevant to the topic at hand. Because of the uncertainty of the initial rental value for water, the model results under different closures for the water market include a sensitivity analysis for these values. The section then concludes with evaluation of the contribution of such a model in predicting water value in this case and in general and how the work could be extended.
109 3.3.2 Southern NV Water CGE Model Choice of Region The region chosen for this model is comprised of five southern Nevada counties. These are Clark County (Las Vegas) and the rural counties of Esmeralda, Nye, Lincoln and White Pine in Nevada. White Pine and Lincoln counties contain the basins to be connected to Las Vegas by the proposed pipeline. As discussed previously, most water‐CGE models are defined at the watershed level for single region models, or are multi‐region models. Fortunately, in this five county region, almost all urban economic activity, such as casino gambling, takes place in Clark County; and almost all the agricultural activity takes place in the rural counties. Because the counties are so specialized economically, the multi‐sector disaggregation mimics the appropriate multi‐region disaggregation. Thus I construct a model that aggregates all the county economies into one ‘region.’ The differential value of water in urban and agricultural sectors when water transfers are not allowed indicates the value of enabling the integration of the water market by the construction of the pipeline. Production/Output As with other water CGE models described in this thesis, I model self‐supplied ‘raw’ water and treated water as two different inputs used by different sectors. Raw water is used by agriculture, mining, electricity, manufacturing, food processing and by the water utility sector. Treated water enters sector production functions as an intermediate commodity. The technological assumption is that treated water is used in fixed proportions with other intermediate inputs in relation to output.
110 In the water utility sector, raw water is combined with the intermediate input aggregate and a CES labor‐capital aggregate in fixed proportions per unit output of treated water, thereby formalizing that a unit of raw water is required to produce each unit of treated water. For all industry sectors except the water utility sector, at the lowest level of the nested production function, capital, labor as well as water, in the relevant sectors, are combined into a CES value‐added aggregate. Value‐added is combined in fixed proportions with the intermediate input aggregate (including treated water from the water utility sector) to produce output. Because there is very little non‐irrigated agriculture in Nevada, land is not considered a substitute for water and is not explicitly modeled as a factor of production. Because land without water is of very low value in Nevada, the effective supply of land for agriculture is limited only by the supply of water. Especially for urban regions, however, this is an oversimplification that could be addressed in future work if land rents by sector could be obtained. Factor Markets Both capital and water supplies are formalized as inelastically fixed in this model. Capital is assumed to be immobile across sectors, but labor is intersectorally mobile. Labor supply is also elastic with respect to wages. The baseline water endowment is measured by the USGS estimates of water withdrawals in southern Nevada (USGS, 2010). I allow for two different closures concerning the mobility of water between sectors. In one scenario water is mobile across all self‐supplying sectors, yielding one
111 equilibrium price for water across all sectors. In the other scenario I assume water is not mobile across sectors, which yields a different price for water in each self‐supplying sector. 3.3.3 Data Water data. Table 3.2 shows the USGS estimates of the distribution of water withdrawals by county and sector in southern Nevada. Las Vegas, in Clark County, is where about 2 million people, or 72%, of the 2012 state population lives (Hardcastle, 2013). In 2005, 64% of total southern Nevada water withdrawals were municipal supplies used by the Clark County population. The majority of the water withdrawals (72%) in the five county region occurred in Clark County, primarily withdrawn from the Colorado River and its reservoir, Lake Mead. The four rural counties had a total population of about 60,000 people in 2012. Most of the water withdrawals in the rural counties neighboring Clark County were used for irrigating hay. Table 3‐2. 2005 Water Withdrawals by county and sector in southern Nevada. USGS Category Public (municipal) supply Domestic self‐supply Industry self‐supply Irrigation Aquaculture/Livestock Mining Thermoelectric Total
Clark 64.0% 2.5% 0.6% 1.9% 0.0% 0.3% 3.0% 72.3%
Esmeralda 0.0% 0.0% 0.0% 3.4% 0.0% 1.5% 0.0% 5.0%
Lincoln 0.1% 0.0% 0.0% 5.8% 0.0% 0.0% 0.0% 6.1%
Nye 0.7% 0.6% 0.0% 6.0% 0.1% 0.7% 0.0% 8.2%
Source: USGS (United States Geological Survey, 2010) and author’s calculations.
White Pine Total 0.6% 65.4% 0.0% 3.2% 0.0% 0.6% 6.9% 24.0% 0.3% 0.5% 0.7% 3.3% 0.0% 3.0% 8.5% 100.0%
112 The five county area is about 49,000 square miles, or about 3,000 square miles bigger than the state of Pennsylvania. Rainfall is sparse but varies across the region with more precipitation on average in the more northern and eastern counties of Lincoln and White Pine (Table 3‐3). The Colorado River is overallocated, and all surface water in the state of Nevada is considered fully or over‐allocated. It is a matter of controversy whether or not groundwater supplies are overallocated in all of the rural counties (Welden, 2003). Water use by detailed industry sector is necessary for the CGE model. The approach Blackhurst et al. (2010) applied to allocate nationwide USGS water use withdrawal estimates across detailed sectors was adapted to allocate sector water use at the county level in southern Nevada, resulting in the estimates shown in Table 3‐4.28 Table 3‐3. Population, resources and resource use by county for southern Nevada Total Water with‐ Water with‐ Avg. inches Land area Population Persons per Withdraw‐ drawals (AF per drawals (AF annual (sq. mi.) 2012 sq. mile als (AF) person) per acre) rainfall 4.1 (Las Clark 7,891 680,756 1,988,195 251.9 0.3 0.13 Vegas) 6.1 Esmeralda 3,582 46,755 860 0.2 54.4 0.02 (Goldfield) 8.8 Lincoln 10,633 57,060 5,100 0.5 11.2 0.01 (Caliente) 4.7 Nye 18,182 76,808 44,292 2.4 1.7 0.01 (Pahrump) White Pine 8,876 79,609 9,945 1.1 8.0 0.01 10.1 (Ely) County
Total 49,164 940,988 2,048,392 41.7 0.5 0.03 Sources: (Hardcastle, 2013, U.S. Census Bureau, 2013, United States Geological Survey, 2010, Western Regional Climate Center, 2013) and author’s calculations.
The largest water withdrawals (615,530 AF) were made to supply municipal utilities in Clark County (Table 3‐4). Using the interindustry and institution purchases 28
The modified method was adopted in such a way that it could potentially be used by any state or county do develop water use intensity factors for water footprints or water CGE models.
113 from IMPLAN data and the assumption of one price, the 615,530 AF was allocated across industries and final demands. Using this estimation, the large casino‐hotel and recreation sector used the largest amount of treated water (67,084 AF, Table 3‐4). The second largest amount of raw water was withdrawn for use in irrigating hay (180,934 AF) in the rural counties. Other major water using sectors were the livestock, metal mining and electricity sectors. Table 3‐4. Water use, employment and value‐added by sector for southern Nevada SECTOR
TREATED WATER WITHDRAWAL (AF) 1,027 2 7 86 13 28 0 416 5,494 0 4,602 664
RAW WATER WITHDRAWAL (AF) 180,934 34,039 9,733 2,565 3,131 4,137 26,522 28,239 0 615,530 0 0
EMPLOYMENT (JOBS)
VALUE ADDED (MILLIONS $)
HAY 284 19.4 LIVESTOCK 284 6.5 DAIRY 72 3.9 VEG & MELON 46 4.6 OTHER AG 435 9.9 OTHER MINING 849 98.9 METAL MINING 1,596 643.1 ELECTRICITY 2,141 1,038.8 TRANSPORT & UTIL 49,739 4,517.2 WATER UTILITY 1,985 184.6 CONSTRUCTION 47,405 3,953.5 RESIDENTIAL CONSTR. 7,776 580.9 BANKS, INSUR., REAL 9,089 0 148,359 17,238.4 ESTATE FOOD PROCESSING 1,366 446 2,848 220.0 OTHER 3,438 5,547 17,964 1,975.2 MANUFACTURING TRADE 6,118 0 134,979 8,187.3 OTHER SERVICES 33,749 0 329,872 23,158.5 HEALTHCARE 5,229 0 57,698 4,299.4 RECREATION 67,084 0 169,410 11,699.4 FOOD&DRINK 11,822 0 107,028 4,195.8 TOTAL 150,234* 910,823** 1,080,770 82,035 *This total does not include exports of treated water and treated water use by government and households. **This total does not include the estimated self‐supplied water use by households and governments of 30,165 AF. Sources: Author’s application of modified Blackhurst methodology(see appendix A) and modified IMPLAN 2010 model for Southern Nevada (Blackhurst et al., 2010a, b, Minnesota IMPLAN Group, 2010).
114 Social Accounting Matrix (SAM) data Money flow data is from the southern Nevada region IMPLAN model, an aggregation of the five county region. Modifications made to the IMPLAN SAM to clean the monetary data for the CGE model are described elsewhere in this thesis. I focus here on the modification most relevant to the sensitivity analysis carried out on initial water rent values. To model water as a factor of production similar to capital or labor, an initial value for water rent is needed. Data on water rents is not available in IMPLAN or BEA data or otherwise routinely collected however, so the rents must be estimated using additional information. The price paid for water was estimated and subtracted from a gross operating surplus account (Other Property Income) provided in the IMPLAN data. Many things affect water factor rents. Water factor rents are related to the value of water rights. Some of the most important aspects affecting the value of water rights in Nevada are its legal status, such as priority date and rulings concerning allocation in the specific water basin, designation of the type of activity the water can be used for, point of diversion, place of use, and whether it is for ground or surface water. Also important are perceptions about future appreciation, economic cycles, and water quality (Miller, 2013; Wichelns, 2010). Some of these are time‐varying, and good data on average returns to water rights is not available, making it difficult to choose a ‘correct’ starting value for water rents. Assuming that the value of a ranch in a very rural and isolated region will be due to the value of the water rights, current sales prices for rural ranches of known size were used to infer low and high values of water (see Table
115 3‐5).29 In all but one set of the sensitivity analyses, it is further assumed that these same values apply in non‐agricultural sectors as well. The range of values roughly reflects asking prices of ranches listed in Table 3‐5, assuming a 3% interest rate to annualize the water rights values. For infinite time periods, where appreciation is assumed to be zero, we assume that permanent water rights sell for their net present value, NPV: NPV
SALE PRICE
RENT i
where i is the interest rate. Table 3‐5 Three Southern Nevada ranch sale advertisements, 2013 Ranch description
Location
Rebel Rock Ranch (scenic canyon) Eden Valley Farm (overallocation of groundwater) Water Canyon Creek (influence of Clark Co. buyouts?)
Caliente, Lincoln Co. Humboldt Co. Lund in White Pine Co.
Asking Price
Asking Price per AF*
158 182.4 AF surface water
$949,900
$5,208
2,294 AF, priority 1976, groundwater
$9,600,000
$1,058
$480,000
$500
Land (acres)
2,877
Water
240 960 AF ground water
Source: Author’s assumptions and calculations, Chris Miller website http://nevadawaterrights.com/2701.html . *With assumption that only water rights determine value of property.
Four different initial price assumptions for the water factor are modeled, from a low price with annualized rent of $15 per acre‐foot (AF), corresponding to water rights that sell for $500/AF, to a high of $3000/AF corresponding to annualized rent of $90 per acre‐foot. One of the four scenarios assumes an initial segmented water market with different prices for different sectors. It was not possible to model a fifth higher water 29
This can in some cases be approximately correct for isolated ranches with little improvement. In one example related by a Nevada real estate agent specializing in water rights sales, water rights accounted for 96% of the value of the ranch. Miller, Chris W. 2013. "Water Rights Values in Nevada," E. Fadali, Phone discussion about determinants of water rights values in Nevada.
116 price because one cannot replicate the baseline levels of output of hay under an assumption that water rights are priced as high as $5,200/AF (Table 3‐5). At that price, annualized rents exceed the baseline value added in the hay sector. Another important question for water‐CGE models is the choice of industry sector aggregation. IMPLAN SAMs include 440 sectors. Although disaggregation is preferable, the highly non‐linear nature of CGE models can make solving that big a system difficult. In Nevada, the most important activities with regard to self‐supplied or raw water use are the water/sewer utility sector, agriculture, mining, and electrical power generation. The hotel‐casino sector is the most important treated water user. Other major treated water users include construction, real estate, golf courses, parks, hospitals, food services and schools. The southern Nevada CGE is thus disaggregated to 20 sectors: hay, livestock, dairy, vegetable and melon farming, other agriculture, metal mining, other mining, electricity, transportation and information, water, residential construction, all other construction, finance and real estate, food processing, manufacturing, trade, healthcare, services, recreation (including casino hotels) and food and drinking places. This sector disaggregation was chosen for several reasons: 1. Type of water use: treated water from the water utility sector is specified in a different way than is self‐supplied water. 2. Nevada’s agricultural sectors, electric utilities, mining and the water and sewer utilities sector remain disaggregated so that the sectors which use large amounts of self‐supplied water are adequately described. Manufacturing and food
117 processing also use self‐supplied water. Other sectors purchase treated water from the water and sewer utility within the input‐output intermediate demands block.30 3. Sectors were grouped together with other sectors that had similar water use intensity. 3.3.4 Scenarios and Sensitivity Analysis Consider a 10% water shortage in southern Nevada. Two types of scenarios are simulated. In one scenario, no mobility between sectors is allowed for the water factor, corresponding roughly to the current situation. In the other, water mobility is allowed, and a market for water arises. This would roughly correspond to a situation in which a pipeline between rural and urban sectors already exists, with the costs of transporting the water paid in a previous period. Sensitivity analyses with respect to the initial value of water is also carried out for each scenario. Each scenario is simulated under a low, medium and a high initial water price, as well the situation in which initial water values vary by sector (because there is no free trade). 3.3.5 Results Not surprisingly, water prices rise much more dramatically in response to a supply reduction when there is no water mobility between sectors (Table 3‐6). Raw water price increase may be as low as 214% (assuming high initial raw water prices) and 30
It would be preferable to split water and sewer into two sectors or, as in some water-CGE models, account for two different commodities: indoor water, which is more expensive since it requires collection and treatment after use, and outdoor water use, which usually is not treated in any way after use. This is an important topic for future research for any water CGE with a substantial focus on commercial and industrial water demands. One of the constraints on water use, for example, in the Reno/Sparks urban area, is that sewer water is subject to very stringent restrictions on pollutant load. The pollutants are very expensive to remove.
118 as high as 1,440% (assuming low initial prices). In the “unified market “ or pipeline scenario, the water supply reduction leads to raw water price increases between 34% and 60%, depending on the intital raw water price assumptions. Table 3‐6. Effect of 10% water supply reduction on raw water rents and treated water prices, with sensitivity analyses
Unified Segmented Market Market
Avg Low Price Avg Mid Price Avg High Price Mixed Low Price Mid Price High Price
Raw water rents ($/AF) Base‐ Sim‐ Dif‐ line ulation ference $15 $231 1440% $54 $257 376% $90 $283 214% $66 $257 289% $15 $24 60% $54 $77 43% $90 $121 34%
Treated Water price ($/AF) Base‐ Sim‐ Dif‐ line ulation ference $971 $1,221 26% $971 $1,221 26% $971 $1,221 26% $971 $1,221 26% $971 $977 1% $971 $989 2% $971 $998 3%
Interestingly, the range between the simulated water factor rents due to the different baseline assumption of initial prices are less (from $231 to $283 or 23%) under the closure that does not allow for mobility than under the closure that does allow for mobility (from $24 to $121 or 404%). The simulated water factor rental rate depends more on initial values where no market is allowed for the water factor between sectors. In the segmented water market case, the raw water price response to the shortage is larger than the variation introduced by initial value assumptions. The component of rents due to the relatively inflexible demand in the water utility sector is more important than the variation introduced by the differing initial assumptions of value. When the water markets are segmented, treated water prices from the water utility increase by 26% when water supply is reduced by 10%, regardless of the initial values of the treated water price. A large proportion of the price of the treated water is
119 due to the other inputs necessary to produce clean water and pipe it to households and businesses, as well as the costs of carrying away the water and treating it again before returning it to the river system. Thus treated water prices do not rise as much proportionally as do the raw water prices. When water markets are integrated, the reduction in water availability has a smaller effect on the price of treated water (1, 2 or 3%; Table 3‐6) that is only slightly sensitive to intital assumptions. The sensitivity is small compared to the range of initial assumptions. Table 3‐7 Rental price for raw water ($/AF)
Hay Livestock Water Utility Metal Mining
baseline simulation difference simulation difference simulation difference simulation difference
Segmented Market low mid high Mixed $15 $54 $90 $15/$90 $17 $59 $97 $17 13% 9% 8% 13% $17 $57 $94 $17 13% 6% 4% 13% $333 $352 $371 $371 2120% 552% 312% 312% $17 $63 $105 $17 13% 17% 17% 13%
Unified Market low mid high $15 $54 $90 $24 $77 $121 60% 43% 34% Same as hay sector
When water is not mobile between sectors, different prices may be paid for water use in each sector. Table 3‐7 displays selected sectoral results. The agricultural sectors in particular do not compete with the water utility sector for raw water. Following the 10% water reduction, raw water factor rents in the agricultural sectors do not increase as much as they do in the water utility sector. For the water utility sector, increases are very large, ranging from about 300% to over 2000% depending on the
120 initial price assumption. The price differentials between the water utility sector in the segmented market and the unified market ranges from $250 to $390 per AF. Table 3‐8 shows the affects on the quantities of water withdrawn for use by each sector. Because raw water factor is supplied perfectly inelastically, total water withdrawals are insensitive to intital price assumptions. When markets are segmented and supplies are reduced by 10%, each sector reduces their water use by 10%. Table 3‐8. Sector water withdrawals under baseline total water withdrawals of 910,823 AF and 10% shortage at 819,740 AF total water withdrawals
Assump‐tion
Segmented Market Low Price
Midprice
180,934
baseline (AF) Simula‐ tion (AF) difference
Water Utility
Unified Market
High Price
Mixed
Low Price
180,934
180,934
180,934
180,934
180,934
180,934
162,840
162,840
162,840
162,840
125,909
127,776
129,131
‐10%
‐10%
‐10%
‐10%
‐30%
‐29%
‐29%
baseline (AF)
615,530
615,530
615,530
615,530
615,530
615,530
615,530
Simula‐ tion (AF) difference
553,977
553,977
553,977
553,977
613,413
609,671
606,913
‐10%
‐10%
‐10%
‐10%
0%
‐1%
‐1%
Live‐stock baseline (AF) Simula‐ tion (AF) difference
34,039
34,039
34,039
34,039
34,039
34,039
34,039
30,635
30,635
30,635
30,635
22,828
20,042
19,388
‐10%
‐10%
‐10%
‐10%
‐33%
‐41%
‐43%
Metal Mining
26,522
26,522
26,522
26,522
26,522
26,522
26,522
23,870
23,870
23,870
23,870
19,071
20,744
21,550
‐10%
‐10%
‐10%
‐10%
‐28%
‐22%
‐19%
Hay
baseline (AF) Simula‐ tion (AF) difference
Midprice
High Price
When supply falls, the price of water rises. If raw water can be moved between sectors, it will be reallocated from the agricultural sectors to the water utility sector. Simulated quantity effects are not as sensitive to assumptions about the initial rental
121 price for the raw water factor as prices are. The livestock sector appears to be the most sensitive. Fifteen percent less raw water is withdrawn by the livestock sector in response to a 10% reduction in raw water availability when a high initial price is assumed, compared to results when the initial price is assumed to be low. A reduction in water availability also effects economic activity in the region. Table 3‐9 shows the simulated affects on employment and value added for the two scenarios under differing initial assumptions about the rental price of raw water. When water markets are segmented, a ten percent water supply reduction causes a loss of about 4,000 jobs (0.4%) from the southern Nevada economy. This estimate is not sensitive to assumptions about the initial price of raw water. When water markets are integrated, and the municipal water utility can purchase agricultural water, employment impacts are less and slightly sensitive to the assumptions about the initial value of raw water. The pattern is very similar for value‐ added. Table 3‐9. Change in employment and value added from baseline levels of 1,080,770 and $86,561,000,000.
Unified Market
Segmented Market
% Assumption Employment % Value‐ increase (jobs) increase added (millions of $) Low Price 1,076,586 ‐0.39% 86,373 ‐0.22% Midprice 1,076,573 ‐0.39% 86,372 ‐0.22% High Price 1,076,575 ‐0.39% 86,371 ‐0.22% Mixed 1,076,615 ‐0.38% 86,374 ‐0.22% Low Price 1,080,611 ‐0.01% 86,554 ‐0.01% Midprice 1,080,301 ‐0.04% 86,540 ‐0.02% High Price 1,080,103 ‐0.06% 86,531 ‐0.04%
122 3.3.6 Discussion Using a model scenario first with and then without a water market between sectors allows estimation of a price differential on an acre‐foot of water of at most $390 in the water utility sector. This price differential could be interpreted as the value of water transported to Clark County through the southern Nevada water pipeline under a 10% water shortage in the southern Nevada region. However, this is the value for one year under one specific water shortage scenario. To estimate whether the value is high enough to support the $3 to $7 billion pipeline cost for would require further study. The value of the pipeline’s water would change each year according to the amount of a water surplus or shortage, increases in population, the amount of water that could be piped to Clark County and other variables. A multi‐period model with stochastic water availability could more fully answer this question. Any environmental damages created by the pipeline would need to be accounted for as a part of the costs of the pipeline. In the current model, demand for treated water is part of a Leontief aggregate bundle of intermediate demands. This specification implies that demand for treated water is highly inelastic. A more realistic specification that allows substitution of other inputs for treated water would decrease the price differential of the water under the shortage scenario. Using a sensitivity analysis, I find that results are generally sensitive to assumptions about the initial rental price for water. Results in this example are most variable under the water factor closure that does not allow for trades between sectors.
123 As data about rental prices for water are often not readily available, a sensitivity analysis seems advisable where the focus of analysis concerns water rent values. The estimated economic consequences of a 10% water shortfall are far smaller than the consequences estimated in the two reports noted earlier. Why doesn’t the CGE model predict the disaster predicted by White or Hobbes et al.? One explanation is that neither of those earlier reports specify the magnitude of the water shortfall. Neither report refers to any measure of water quantity, water use by sector, by household, elasticity of substitution of water for other factors, or price elasticities of demand for water. Instead, in the White report, an assumption is made that an unspecified water shortage will cause all building permits to be suspended and 30% of construction workers to immediately lose their jobs. The Hobbes report estimates the consequences of an assumed sudden downturn in construction due to any reason. There is no explanation of how construction is affected by changes in the price of water, much less any attempt to estimate how the price of water may change if availability changes. In sum, neither price nor quantity, and neither supply nor demand for water were formalized to prepare the first two reports. In contrast, the water CGE explictly models all these factors. The CGE model has the realistic features that prices change when supplies change, and that businesses and people respond to price changes. These adaptive responses mean that the consequences of a water shortage are spread and shared and softened. And, if the current CGE model formalized that a minimum amount of water rights must be available for purchase per home built (as is required by regulation in northern Nevada, depending
124 on the type of home‐‐ single or multi‐family) this would more realistically link water availability to construction activity, and a more significant effect of water supply reductions on the construction sector, which would also reverberate through the whole economy. Note also that the current model is not multi‐period model, so it not well‐suited for modeling alternative investment decisions such as those needed for water infrastructure planning and the construction industry. Future work on this model may include specifying multi‐periods, based on the water that might realistically be required for the construction sector. In addition, the CGE model could allow for in‐ or out‐ migration of labor from the region depending on regional wages relative to the rest of the domestic economy, and perhaps relative to per capita water availability as amenity as well. Also, it would be a better model if accurate estimates of the price elasticity of demand (by type of user) and the elasticities of substitution for water for other factors (by sector) could be used. It would also be appropriate to explicitly account for the costs of transfering water from rural to urban markets. The simulation results are also sensistive to initial value of raw water rents, so it would be helpful to be able to parameterize the model using more information about actual raw water rents. In the southern Nevada water CGE model the water utility sector and the raw water factor were specified as perfectly competitive sectors. In reality the water utility sector is a quasi‐public entity. Formalizing the utility as something other than a profit‐ maximizer will result in a different model and, potentially, different simulation outcomes.
125 Production functions could be better specified for the self‐supplied water factor and treated water intermediate input. Treated water could be formalized as a CES aggregate of value added and other inputs to allow for substitutions in response to price changes. Similarly, untreated water could be combined in a multi‐level nest with capital in a CES production function to allow for different elasticities of substitution between factors. The CES production functions and current assumptions about the elasticity of substitution imply values for the price elasticity of demand for water as an input. These could eventually be updated using econometrically estimated elasticities using the data in the Rollins‐Stoddard Nevada water utility data, currently under development at the University of Nevada.
126 3.4
Discussion and Conclusions There is no single “value of water in production.” Where water markets are
segmented, the value may differ sector to sector. Even where water markets are integrated, the market price of water will adjust to shifts in water supply, demand, changes in input, factor, or output prices, incomes, government policies and institutions. CGE modeling helps us understand these values and the reasons why they may differ or change. Water‐CGE models are uniquely better‐suited to exploring the economic effects of out‐of‐sample variations, such as reductions in water availability due to global warming. This is because CGE model are based on fundamental structural assumptions. CGE modeling may be the only way to understand important indirect effects, which can affect the value of water in production and are important when non‐marginal changes are under consideration. Factor prices, input prices, output prices, household incomes, and government taxes are all taken into account in a CGE model. A CGE model can indicate winners and losers as a result of changes, and provide measures of welfare changes induced by policy changes.
127 3.5 References Berck, Peter; Sherman Robinson and George Goldman. 1991. "The Use of Computable General Equilibrium Models to Assess Water Policies" In The Economics and Management of Water and Drainage in Agriculture., ed. P. Berck, S. Robinson and G. Goldman, 489‐509. Norwell, MA: Kluwer Academic Publishing. Berrittella, M.; A. Y. Hoekstra; K. Rehdanz; R. Roson and R. S. J. Tol. 2007. "The Economic Impact of Restricted Water Supply: A Computable General Equilibrium Analysis." Water Research, 41(8). Berrittella, M.; K. Rehdanz; R. Roson and R. S. J. Tol. 2008. "The Economic Impact of Water Taxes: A Computable General Equilibrium Analysis with an International Data Set." Water Policy, 10(3). Berrittella, M.; K. Rehdanz and R. S. J. Tol. 2006. "The Economic Impact of the South‐ North Water Transfer Project in China: A Computable General Equilibrium Analysis" Working Paper FNU‐117. Palermo, Italy: Fondazione Eni Enrico Mattei. Blackhurst, Michael; Chris Hendrickson and Jordi Sels i Vidal. 2010a. "Direct and Indirect Water Withdrawals for U.S. Industrial Sectors." Environmental Science and Technology, 44(6), pp. 2136‐30. ____. 2010b. "Direct and Indirect Water Withdrawals for U.S. Industrial Sectors, Supplemental Material." Environmental Science and Technology, 44(6), pp. 2136‐30. Brean, Henry. 2011. "Water Authority: New Report of $7.3 Billion Pipeline Cost Is Worst‐Case Analysis." In Las Vegas Review‐Journal. Las Vegas, NV. Brean, Henry. 2013. "Southern Nevada Water Authority's Pipeline Plan Draws Fire During Ely Hearing." In Las Vegas Reveiw‐Journal. Las Vegas, Nevada. Brouwer, Roy; Marjan Hofkes and Vincent Linderhof. 2008. "General Equilibrium Modelling of the Direct and Indirect Economic Impacts of Water Quality Improvements in the Netherlands at National and River Basin Scale." Ecological Economics, 66(1), pp. 127‐40. Calzadilla, A.; K. Rehdanz and R. S. J. Tol. 2010. "The Economic Impact of More Sustainable Water Use in Agriculture: A Computable General Equilibrium Analysis." Journal of Hydrology, 384(3‐4), pp. 292‐305.
128 Calzadilla, A.; K. Rehdanz and R. S. J. Tol.. 2011. "Water Scarcity and the Impact of Improved Irrigation Management: A Computable General Equilibrium Analysis." Agricultural Economics, 42(3), pp. 305‐23. Diao, Xinshen and Terry Roe. 2003. "Can a Water Market Avert the "Double‐Whammy" of Trade Reform and Lead to a "Win‐Win" Outcome?" Journal of Environmental Economics and Management, 45, pp. 708‐23. Diao, Xinshen and Terry Roe. 2000a. "Economy‐Wide Gains from Decentralizing Water Allocation in a Spatially Heterogeneous Agricultural Economy.," In The Political Economy of Water Pricing Reforms., ed. A. Dinar. New York: Oxford University Press. Diao, Xinshen and Terry Roe. 2000b. "The Win‐Win Effect of Joint Water Market and Trade Reform on Interest Groups in Irrigated Agriculture in Morocco" In The Political Economy of Water Pricing Reforms, ed. A. Dinar. Oxford: Oxford University Press. Diao, Xinshen; Terry Roe and Rachid Doukkali. 2005a. "Economy‐Wide Gains from Decentralized Water Allocation in a Spatially Heterogenous Agricultural Economy." Environment and Development Economics, 10(3), pp. 249‐69. Dixon, P. B.; M. T. Rimmer and G. Wittwer. 2011. "Saving the Southern Murray‐Darling Basin: The Economic Effects of a Buyback of Irrigation Water." Economic Record, 87(276), pp. 153‐68. Dixon, Peter B. 1990. "A General Equilibrium Approach to Public Utility Pricing: Determining Prices for a Water Authority." Journal of Policy Modeling, 12(4), pp. 745‐67. Fadali, Elizabeth. 2012. "Implan Water Footprints," In. Minneapolis, MN: Mid‐continent Regional Science Association 43rd Annual Conference. Fadali, Elizabeth. 2012. "The Economics of the Regional Water Footprint," In Western Regional Science Association. Poipu, Hawaii. Ghadimi, Hodjat 2007. "CGE Modeling and Applications: A Short Course," Morgantown, West Virginia. http://rri.wvu.edu/CGECourse/index.htm Gillig, Dhazn and Bruce A. McCarl. 2002. "Introduction to Computable General Equilibrium Model (CGE)" College Station, TX. Goodman, D Jay. 2000. "More Reservoirs or Transfers? A Computable General Equilibrium Analysis of Projected Water Shortages in the Arkansas River Basin." Journal of Agricultural and Resource Economics, 25(2), pp. 698‐713.
129 Gomez, C.; D. Tirado and J. Rey‐Maquieira. 2004. "Water Exchanges Versus Water Works: Insights from a Computable General Equilibrium Model for the Balearic Islands." Water Resources Research, 40(10). Green, Emily. 2012. "Tunneling under California's Bay: Delta Water Wars" In High Country News. Paonia, CA. Hardcastle, Jeff. 2013. "Nevada State Demographer 2012 Estimates" Reno, Nevada: College of Business Business Service Group University of Nevada Reno. Hassan, R. and J. Thurlow. 2011. "Macro‐Micro Feedback Links of Water Management in South Africa: CGE Analyses of Selected Policy Regimes." Agricultural Economics, 42(2), pp. 235‐47. Hassan, R.; J. Thurlow; T. Roe; X. Diao; S. Chumi and Y. Tsur. 2008. "Macro‐Micro Feedback Links of Water Management in South Africa: CGE Analyses of Selected Policy Regimes." The World Bank, Policy Research Working Paper Series: 4768. Hobbs, Guy; John Bonow and Jeremy Aguero. 2004. "The Impact of a Growth Interruption in Southern Nevada" O. Hobbs, and Associates, Inc. Las Vegas, Nevada: Prepared for Southern Nevada Water Authority. Horridge, Mark; Peter B Dixon and Maureen T. Rimmer. 1993. "Water Pricing and Investment in Melbourne: General Equilibrium Analysis with Uncertain Streamflow" pp. 1‐25. Clayton, Australia: Centre of Policy Studies, Monash University. Horridge, Mark; John Madden and Glyn Wittwer. 2005. "The Impact of the 2002‐2003 Drought on Australia." Journal of Policy Modeling, 27(3), pp. 285‐308. Kenny, J. F., Barber, N. L., Hutson, S. S., Linsey, K. S., Lovelace, J. K., & Maupin, M. A. (2009). Estimated Use of Water in the United States in 2005. nvco2005.xls, from United States Geological Survey, http://water.usgs.gov/watuse/data/. Letsoalo, A.; J. Blignaut; T. de Wet; M. de Wit; S. Hess; R. S. J. Tol and J. van Heerden. 2007. "Triple Dividends of Water Consumption Charges in South Africa." Water Resources Research, 43(5). Lindall, Scott; Doug Olson and Greg Alward. 2006. "Deriving Multi‐Regional Models Using the Implan National Trade Flows Model." Regional Analysis & Policy, 36(1), pp. 76‐ 83.
130 McChesney, John. 2011. Nevada Water Maven: “I Would Not Declare the Drought over on the Colorado River” In John McChesney's Blog. Palo Alto, CA: Stanford University Rural West Initiative Bill Lane Center for the American West. Miller, Chris W. 2013. "Water Rights Values in Nevada," personal communication with E. Fadali, Phone discussion about determinants of water rights values in Nevada. Minnesota IMPLAN Group. 2010. "Version 3.0 User's Guide" Hudson, WI: Minnesota IMPLAN Group, Inc. Mubako, Stanley. 2011. "Frameworks for Estimating Virtual Water Flows among U.S. States" Department of Environmental Resources and Policy, Carbondale, IL: Southern Illinois University. Mukherjee, Natasha. 1996. "Water and Land in South Africa: Economy‐Wide Impacts of Reform: a Case Study for the Olifants River" In TMD Discussion Papers. Washington, D.C.: International Food Policy Research Institute Trade and Macroeconomics Division. National Agricultural Statistics Service. 2009. "2007 Census of Agriculture Nevada State and County Data" Washington, D.C.: United States Department of Agriculture. National Agricultural Statistics Service. 2008. "2008 Farm and Ranch Irrigation Survey" United States Department of Agriculture. Peterson, Deborah; Gavan Dwyer; David Appels and Jane Fry. 2005. "Water Trade in the Southern Murray‐Darling Basin." Economic Record, 81 (Special Issue Aug. 2005), pp. 115‐27. Qureshi, Muhammad Ejaz; Wendy Proctor; Mike D. Young and Glyn Wittwer. 2012. "The Economic Impact of Increased Water Demand in Australia: A Computable General Equilibrium Analysis." Economic Papers, 31(1), pp. 87‐102. Renzetti, S. 2005. "Determining the Economic Value of Water: Concepts and Methods. Resources for the Future." Environmental & Resource Economics, 32(3), pp. 439‐41. Robinson, Sherman and Clemen Gehlhar. 1995. "Land, Water, and Agriculture in Egypt: The Economywide Impact of Policy Reform." TMD Discussion Paper No. 1, 1‐50. Washington, D.C.: International Food Policy Research Institute. Roe, T.; A. Dinar; Y. Tsur and X. S. Diao. 2005. "Feedback Links between Economy‐Wide and Farm‐Level Policies: With Application to Irrigation Water Management in Morocco." Journal of Policy Modeling, 27(8), pp. 905‐28.
131 Rose, A. and S. Y. Liao. 2005. "Modeling Regional Economic Resilience to Disasters: A Computable General Equilibrium Analysis of Water Service Disruptions." Journal of Regional Science, 45(1), pp. 75‐112. Rose, A.; S. Y. Liao and A. Bonneau. 2011. "Regional Economic Impacts of a Verdugo Scenario Earthquake Disruption of Los Angeles Water Supplies: A Computable General Equilibrium Analysis." Earthquake Spectra, 27(3), pp. 881‐906. Roson, Roberto and Martina Sartori. 2010. "Water Scarcity and Virtual Water Trade in the Mediterranean." Working Paper, Department of Economics, pp. 1‐13. Venice, Italy: Ca' Foscari University of Venice. Rourke, Brian C. 2009. "2006 Community Water System Survey: Volume 1," Office of Groundwater and Drinking Water. Environmental Protection Agency. Schreider, S. . 2009. "Water Price Dynamics, Water Derivatives and General Equilibrium Modelling." In 18th World IMACS/ MODSIM Congress. Cairns, Australia. Seung, C., T. Harris and R. Narayanan. 1998. "A Computable General Equilibrium Approach to Surface Water Reallocation Policy in Rural Nevada." American Journal of Agricultural Economics, 80(5), pp. 1197‐97. Seung, C. K.; J. Englin and T. Harris. 1997. "Application of Computable General Equilibrium Model to Derive Impacts of Surface Water Reallocation Policy." Journal of Agricultural and Resource Economics, 22(2), pp. 365‐95. Seung, C. K.; T. R. Harris; J. E. Englin and N. R. Netusil. 2000. "Impacts of Water Reallocation: A Combined Computable General Equilibrium and Recreation Demand Model Approach." Annals of Regional Science, 34(4), pp. 473‐87. Seung, Chang; Thomas R. Harris; Thomas R. MacDiarmid and W. Douglass Shaw. 1998. "Economic Impacts of Water Reallocation: A CGE Analysis for the Walker River Basin of Nevada and California." Journal of Regional Analysis and Policy, 28(2), pp. 13‐34. Seung, Chang K. 1999. "Application of a Computable General Equilibrium Model to Evaluate Surface Water Reallocation Policies." Review of Regional Studies, 29(2), pp. 139‐55. Seung, Chang K.; Thomas R. Harris and Thomas R. MacDiarmid. 1997. "Economic Impacts of Surface Water Reallocation Policies: A Comparison of Supply‐Determined Sam and CGE Models." Journal of Regional Analysis and Policy, 27(2), pp. 55‐76.
132 Smith, C. A., Simon, A. J., & Belles, R. D. (2011). Estimated Water Flows in 2005. (LLNL‐ TR‐475772). Livermore, CA: Retrieved from https://flowcharts.llnl.gov/. Tsur, Yacov; Terry Roe; Rachid Doukkali and Ariel Dinar. 2004. "Chapter 5. Interaction between Economywide Policies and Irrigated Agriculture in Morocco" In Pricing Irrigation Water: Principles and Cases from Developing Countries. Washington, D.C.: Resources for the Future. United Nations Environmental Policy. 2008. "Vital Water Graphics ‐ an Overview of the State of the World’s Fresh and Marine Waters." Nairobi, Kenya. U.S. Census Bureau. 2013. "State and County Quick Facts," Data derived from Population Estimates, American Community Survey, Census of Population and Housing, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits Department of Commerce. United States Geological Survey. 2010. "Estimated Use of Water in the United States County‐Level Data for 2005." U. S. Department of the Interior. United States Geological Survey. 2010. "Annual Water Data Reports." U. S. Department of the Interior, National Water Information System. http://wdr.water.usgs.gov/ van Heerden, J. H.; J. Blignaut and M. Horridge. 2008. "Integrated Water and Economic Modelling of the Impacts of Water Market Instruments on the South African Economy." Ecological Economics, 66(1), pp. 105‐16. Watson, Philip S. and Stephen Davies. 2011. "Modeling the Effects of Population Growth on Water Resources: A CGE Analysis of the South Platte River Basin in Colorado." Annals of Regional Science, 46(2), pp. 331‐48. Welden, Fred W. 2003. "History of Water Law in Nevada and the Western States." Background paper 03‐2, 1‐15. Carson City, Nevada: Legislative Council Bureau. Weisburg, Jenny. 1997. "Average Annual Precipitation, Nevada" Plot of 1961‐90 annual average precipitation contours from Cooperative Stations with Chistopher Daly's generation of PRISM model estimates. USDA‐NRCS National Water and Climate Center. Western Regional Climate Center. 2013. "Cooperative Climatological Data Summaries NOAA Cooperative Stations ‐ Temperature and Precipitation."
133 White, William T.; Thomas Carroll, M. and R. Keith Schwer. 1992. "The Impact of a Water Imposed Interruption of Growth in the Las Vegas Region." W. T. W. Associates. Las Vegas, Nevada: Prepared for the Las Vegas Valley Water District. Wichelns, Dennis. 2010. "Agricultural Water Pricing: United States" In Sustainable Management of Water Resources in Agriculture, ed. OECD. Hanover, Indiana: Hanover College. Wing, Ian Sue. 2011. "Computable General Equilibrium Models for the Analysis of Economy‐Environment Interactions" In Research Tools in Natural Resource and Environmental Economics, ed. A. Batabyal and P. Nijkamp. Boston: World Scientific Publishing Company. Wittwer, Glyn. 2012. "Using Water as a Factor of Production" personal communication with E. Fadali, e‐mail correspondence concerning how to estimate initial rents for water factor and incorporate into SAM. Wittwer, G. ed. Economic Modeling of Water: The Australian CGE Experience. Dordrecht: Springer, 2012. Wittwer, G. and M. Griffith. 2012. "The Economic Consequences of a Prolonged Drought in the Southern Murray‐Darling Basin" In Economic Modeling of Water: The Australian Experience, ed. G. Wittwer, 119‐41. Dordrecht: Springer. Wittwer, G. and M. Griffith. 2011. "Modelling Drought and Recovery in the Southern Murray‐Darling Basin." Australian Journal of Agricultural and Resource Economics, 55(3), pp. 342‐59. Wittwer, Glyn. 2011. "Confusing Policy and Catastrophe: Buybacks and Drought in the Murray‐Darling Basin." Economic Papers, 30(3), pp. 289‐95. Wittwer, Glyn. 2009. "The Economic Impacts of a New Dam in South‐East Queensland." Australian Economic Review, 42(1), pp. 12‐23. Wittwer, Glyn. 2006. "Modelling Future Urban and Rural Water Requirements in a CGE Framework" pp. 1‐19. Centre of Policy Studies, Monash University.
134
4 4.1
Comparing policy advice using a CGE model that operationalizes virtual water flows and water footprints for the state of Nevada Introduction Nevada is the most arid state in the United States, with average precipitation of
less than nine inches a year (National Oceanic and Atmospheric Administration, 2012). Precipitation falls mainly in the winter season as snow on high elevation mountains, providing a store of water that can be used for irrigation agriculture and municipal demands throughout the summer. A Bureau of Reclamation report predicts shortfalls in snowpack and water deliveries for both the northern Truckee‐Carson and the Colorado watersheds in the next two decades (Alexander et al., 2011). The Colorado river and the groundwater that is recharged by it represents over 90% of the water supply for Las Vegas in the south (McChesney, 2011). Similarly, the Truckee and Carson rivers in the north supply about 90% of the water for Reno, Sparks and Carson City (Truckee Meadows Regional Planning Agency, 2008). These two urban regions represent 90% of the state’s population (Nevada State Demographer, 2013). Accordingly, the Bureau of Reclamation has rated the potential of conflict over water in both these areas as highly likely within the next two decades (Bureau of Reclamation, 2003). Given the hydrologic similarities between the state of Nevada and arid Middle Eastern countries such as Egypt and Jordan, it is possible that the strategy these countries have adopted of subsidizing imports of water intensive agricultural commodities could be beneficial to Nevada as well. We can learn about the efficacy of these policies by simulating their effects using a water‐CGE model of Nevada.
135 On the other hand, local food advocates believe that communities that obtain more food from local farms will reap multiple benefits such as greater control over food quality, reduced pollutants, improved nutrition, and control over regional water sources, as well as a boost to the local economy (Masi et al., 2010). Some local food advocates also claim that encouragement of sourcing food supplies locally will create greater resiliency against rising energy prices or climate change. This strategy is the opposite of the previously noted one. Should Nevada discourage imports of agricultural goods? Which strategy would allow Nevadans to be the most food secure in a changing climate? Which strategy is most sustainable in terms of lowest water use? Which is more economically robust in terms of employment rates and household incomes? In this paper I develop and apply a computable general equilibrium model that operationalizes water footprint and virtual water trade flow data to investigate both the economic and water resource use outcomes for these two policy strategies: the “Arid Country” advice which encourages importation of water intensive goods, or the “Local Food” advice which discourages importation of agricultural goods. Both policies are examined under conditions meant to simulate global warming, with and without a water “market.” 4.2 Water Footprints, Virtual Water and Water CGE Models 4.2.1 Virtual Water First published by Allen (1997), the term virtual water has been used to denote the water used to produce, though no longer present physically in, a crop or product such as grain or blue jeans. Using a similar concept, Fishelson wrote about the ‘water,
136 food and trade nexus’ with regard to water short countries in the Middle East and North Africa, pointing out that food imports into the arid region greatly reduce demand for water and thus reduce the possibility of conflict over the scarce resource (Fishelson, 1989). The term has also been used to mean a policy of promoting importation of agricultural commodities to reduce water demand and sometimes also the reduction of agricultural exports from countries with relative water scarcity (Kumar and Singh, 2005, Reimer, 2012, Wichelns, 2010, Yang and Zehnder, 2007). In some cases, this ‘virtual water’ advice, construed as being derived from the two‐good, two‐factor Heckscher‐ Ohlin trade model, has been overly simplified as general advice for water short countries to always import water intensive products from countries with more abundant water resources and decrease export of water intensive products. As Wichelns and Reimer both point out, this advice ignores many complexities. Wichelns criticizes ‘virtual water’ policies as not being based on a conceptual framework that can help to formulate policy because it focuses on only one resource endowment, water. Reimer shows that while water conceivably can be a source of comparative advantage, water is only one of many usually more expensive factors. Kumar and Singh analyze virtual water trade in 146 countries and find that there is no correlation between water resource abundance and low virtual water trade balances; rather, trade in agricultural commodities depends on many resource endowments in addition to water such as soil and land. Yang and Zehnder find that virtual water ‘policy’ should be only one of a number of strategies used by water scarce countries.
137 4.2.2 Water Footprints A related vein of literature, which I will call water footprint literature, uses the concept of virtual water flows also, but emphasizes the consumption side of virtual water trade. The term water footprint was first used by Hoekstra et al. in 2002 (Hoekstra, 2003) and has been further defined and elaborated by the same group since then (Hoekstra et al., 2009). An example of the water footprint would be the footprint of a country. The footprint of a country measures the water directly consumed within the country, but subtracts the virtual water in exports and adds the virtual water in imports to that total (Hoekstra and Chapagain, 2007). In contrast with the virtual water literature, the water footprint literature is more closely aligned with the eco‐footprint and carbon footprint concepts and often carries a stated or unstated concern over sustainable use of resources (Hoekstra, 2009). Because of this emphasis on sustainability, a large water footprint for a region with net imports of virtual water has sometimes been seen as evidence of unsustainable lifestyles and a need to reduce these imports, thus reducing water related externalities in other regions, in contrast to the ‘virtual water’ policy described previously. For example, in their analysis of the cotton trade, Chapagain et al. find that European Union consumers of cotton can be indirectly held responsible for about 20% of the water deficit that caused the desiccation of the Aral Sea, thereby tracing environmental impacts of a product back to the consumer (Chapagain et al., 2006). The concern implied in some of the footprint literature is loosely related to the concerns of the local food movement, which promotes the idea of sustainable resource use as well as regional self‐sufficiency. For example, an Ohio
138 coalition is promoting “The 25% Shift”, a campaign to source at least 25% of regional food consumption from regional sources. The literature promoting this vision suggests that such a shift will provide jobs and increase food security while lowering the carbon footprint of the region and improving air and water quality (Masi et al. 2010). The water footprint policy advice directed towards the consumer of virtual water has also been the subject of criticism. In a comprehensive summary of the footprint’s lack of economic foundations, Alistair Watson argues that virtual water and water footprints cannot provide useful policy advice (Frontier Economics, 2008). He states that the footprint focuses on only one factor of production, can give no indication of the opportunity cost and the endowments of water in a given time and place, and does not take into account different production technologies and the relationship between water and all other inputs to production, amongst other failings. However, I argue that the water footprint concept, along with the carbon footprint and other similar descriptive measures that take into account environmental trade balances, help us to view resource use from the holistic perspective that is necessary for solving certain types of resource allocation and environmental externality problems. For example, one cannot measure the water or energy efficiency of an economy over time correctly without measuring the virtual flow of water or energy into and out of the economy in its exports and imports, just as one cannot take a full account of an economy without examining its dollar trade balance. Similarly, national or regional policies regarding water resources, such as
139 providing irrigation water subsidies, may flow across borders. The incidence of these subsidies may be of interest to the political entity providing the subsidy. Virtual water flows can be calculated using input‐output tables of purchases and sales in combination with sectoral water intensity factors, although the official methodology recommended by Hoekstra et al. uses life cycle analysis techniques (Hoekstra et al. 2009). Dietzenbacher and Velazquez demonstrate this alternate input‐ output methodology for the Andalusian region of Spain (Dietzenbacher and Velazquez, 2007). Using data on total water use by sector, water consumption per Euro of output is calculated and used to find virtual water multipliers. The input‐output matrix includes data on imports and exports from other regions. Using this data, flows of virtual water can be calculated, and water use can be attributed to the appropriate final demand users, including users from outside the region. 4.2.3 Water CGE Models The computable general equilibrium model is a natural extension of the input output data framework. In contrast to the footprint and virtual water measures, a CGE model can be used to examine trade‐offs between many different inputs to production and to specify endowments for these resources so that opportunity costs are taken into account. Water resources can be incorporated into CGE models. For a recent review of water‐CGE models see Fadali et al. (2012). Several water‐CGE models use a virtual water or water footprint concept. One study, (Berrittella, et al., 2007), used an adaptation of the Purdue University GTAP (Global Trade Analysis Project) multi‐regional model of the world economy to explore
140 the economic consequences of a world‐wide water shortage on virtual water trade. Berrittella et al. (2008) use the same model to analyze the impacts on world trade in water intensive goods due to changing water prices. GTAP is also used by Calzadilla et al. (2011) to model world‐wide improved water efficiency. They find that there would be winners and losers to such improvements as comparative advantage and terms of trade shift. These papers appear to be the only published studies that use a CGE model in conjunction with an explicit virtual water concept. Several other unpublished working papers using virtual water in conjunction with a CGE model exist. For example, Rosen and Sartori use the same modified GTAP model to better understand how coming climate changes will impact the Mediterranean region’s economy (Roson and Sartori, 2010). Castellano et al. use a CGE model of the Huesca region of Spain to investigate the results of the EU Water Framework Directive which reforms irrigation water prices so that they will cover all costs (Castellano et al., 2010). They use a CGE model developed by IFPRI (International Food and Policy Research Institute). Policy scenarios include differing ways of assigning water costs to either producers or final demand users so it is useful to be able to attribute indirect water use to its users. Other types of environmental trade balances have been examined with CGE models. For example, in another working paper, Turner et al 2009, has used a multi‐ regional model of Scotland and the rest of UK to track the changes in pollution flows due to an increase in export demand in rest of UK (Turner, et al., 2009) and similarly Turner
141 et al. (2011) use a CGE model of Wales and rest of UK to examine the effects of an increase in metal manufacturing. Using the water‐CGE model I am able to empirically test both the “Arid Country” virtual water advice to increase imports of water intensive goods and the “Local Foods” prescription to decrease imports of agricultural goods. A CGE model can take into account more complexity than can the Heckscher‐Ohlin two‐trade two‐factor trade model or a measurement of virtual water flows. The policy vehicle used to implement the two differing sets of advice is a subsidy or tariff on agricultural and food processing imports. Both are tested under conditions meant to simulate global warming: a 20% shortage of water deliveries in combination with an increase in the world price of agricultural commodities. 4.3
Model Overview The water CGE model used to carry out the simulations is a 20 sector static
model of the state of Nevada. Agricultural sectors include hay, beef and dairy, Nevada’s three largest agriculture sectors by value of output, the vegetable and melon sector and an all other agricultural activities sector. Other sectors are metal mining, all other mining, electric power, transportation, gas and information, water utility, residential construction, all other construction, finance and real estate, food processing, manufacturing, trade, healthcare, recreation including casino hotels, food services and all other services.
142 The GAMS computer code for the model is based on the Washington State University regional CGE model available for download at http://www.agribusiness‐ mgmt.wsu.edu/Holland_model/ and is documented in Stodick et al. (2004). The solver used is CONOPT. The model is built in the tradition of the Lofgren IFPRI model (Lofgren et al., 2002). The model has been modified in several ways to facilitate the incorporation of water resources and the scenarios investigated in this paper. 4.3.1 Production Water is a factor of production along with labor and capital. This water factor is considered “raw” self‐supplied water. Rights to annual water withdrawals from surface or groundwater require rents which are paid out to water rights owners as one would pay out land rents. Only certain sectors self‐supply water: the agricultural sectors, mining, electricity, food processors and some other manufacturers and the water utility sector. The water utility sector also uses raw water to produce treated water for sale to all other industry and final demand sectors. Thus the water utility commodity is different than raw water and it is sold at a different price. It is a commodity rather than a factor of production, and it is part of the intermediate input bundle. All sectors’ production technologies except for the water utility sector are a Leontief‐CES nest as illustrated in Figure 1. In the upper level of the nest, labor, capital and raw water are combined into a CES value‐added composite. The elasticity of substitution for all sectors is 0.7, a value that allows for a degree of substitution between factors that is a little less elastic than a Cobb‐Douglas specification. The value
143 Figure 4‐1. Production technology for all sectors excepting water utility
added and intermediate use bundle are combined in fixed proportions to produce output. In contrast, in the water utility sector production function, the raw water factor is combined in fixed proportions with the labor‐capital composite and the intermediate inputs bundle to produce output (see Figure 2). This ensures that for each unit of water commodity produced, a unit of water factor must be rented, which gives a water balance for this sector. Since this is a regional model, intermediate commodity demand is an Armington composite of imported and domestic inputs and imports may come from either a foreign or domestic source. 4.3.2 Final Demands Three income levels of households, an investment sector, a combined state and local government, a federal government, and both domestic and foreign export
144 customers all demand final goods. Their purchases net of imports give the final demand version of gross regional product: C + I + G + X – M. Figure 4‐2. Water utility production function
4.3.3 Households Households are assumed to demand final goods to maximize a Stone‐Geary utility function subject to the budget constraint consisting of income earned from supplying labor, capital and water plus transfers from other institutions. Stone‐Geary allows for non‐homothetic preferences, which in turn allows for non‐unitary income elasticities and non‐zero cross‐elasticities. Subsistence consumption levels for food as well as the treated water utility commodity are of particular interest in this model because food and water are necessary for survival, and water is the focus of the model. Estimates of the subsistence levels are found from the Frisch parameter and income
145 elasticities from the literature, as well as using author’s judgment. The author’s judgement is based on estimates at different income levels in Lluch et al. (1977) and Berck et al. (1996). The Frisch parameter is the inverse of the ratio of discretionary income to total expenditure. Discretionary income is what is left of total expenditure after taxes, saving, and all subsistence expenditure. A Frisch value of ‐1 would mean there is no subsistence expenditure. The higher a Frisch parameter is in absolute value, the lower the income level if there is a declining marginal utility of expenditure (Lahiri et al., 2000, Lluch et al., 1977). Table 4‐1 gives the Frisch parameters and income elasticities in the model. Table 4‐1. Nevada Footprint Water‐CGE exogenously chosen parameters Description
Symbol
Value
Frisch low income Frisch middle income Frisch high income
FrischL FrischM FrischH
‐1.9 ‐1.5 ‐1.1
Income elasticity for processed food
IneFOODPROC,H
0.33
IneVEGMEL,H
1.1
IneTRADEL,H
0.55
IneELEC,,H
0.52
IneMFG,,H
1.5
IneSERL,H
1.4
Income elasticity for vegetables and melons Income elasticity for retail and wholesale trade goods Income elasticity for electricity Income elasticity for manufactured goods Income elasticity for other services Income elasticity, all other commodities
IneC,H
1
4.3.4 Government Because this is a regional model I distinguish two levels of government, a combined state/local government and the federal government. Both levels of
146 government receive ad valorem taxes on wages and rents paid to factors, indirect taxes from sector activities (ad valorem), and income taxes, also paid ad valorem. For this model, the state/local government also can collect tariffs or pay subsidies. The federal government budget deficit or surplus is endogenous. The federal government borrows (or repays) in order to support its baseline level purchases at current prices when its revenue is not enough (too much). The state/local government must balance its budget. Given its endogenous revenue, it adjusts its overall spending up or down proportionally to hold its budget balance constant. 4.3.5 Exports and Imports Nevada is an arid state with a small population. For the water intensive agricultural goods under primary consideration, Nevada has little market power. Thus I model Nevada as a “small country” with respect to both world import and export prices. This means that import supplies prices and exports demands prices from outside Nevada are exogenous. Because it is a regional model, there are exports to both the rest of the U.S. (RUS) and the rest of the world (ROW). Commodities produced for export are imperfect substitutes with the variety of the commodity produced for local consumption (the Armington Assumption). Producers choose the mix to supply to export and local markets to maximize profit subject to a two level nested constant elasticity of transformation (CET) function specification. The first level of the nest distinguishes ROW and RUS exports in an aggregate while the second distinguishes this aggregate from
147 goods supplied for local consumption. According to Holland (2010), the elasticity in the CET for exports within the US should be high since it is easy to export inside the country. Likewise the elasticity in the CET should be lower for foreign exports which reflects greater differentiation, including distinct temporal/seasonal availability. Unfortunately, the existing nesting structure (Figure 4‐3) does not easily accommodate these insights. Esube, the elasticity of transformation for ROW and RUS exports, was set at 2.0 for all commodities except the residential housing construction and water utility sectors, a moderately elastic choice. Residential housing construction and water utility sectors were set at 0.5, a lower elasticity level chosen to better represent these ‘non‐tradable’ sectors. Esubs, the elasticity of transformation for composite exports and domestic output was set at 2.5 for all but the non‐tradable sectors, a more elastic choice. Figure 4‐3 Regional Supply
CET function associated with esubs
QX (total regional output)
QE (composite exports) CET function associated with esube
QERFT (Rest of World exports)
QD (domestic output sold domestically)
QERDT (Rest of U.S. exports)
Imports are specified in a similar manner (see Figure 4). Because of sector aggregation and the necessarily more narrow production in any given sector, industry
148 imports are likely to have a low elasticity of substitution ((David Holland, 2010). Relative changes in price will tend to have less effect on a broad category of goods versus a single narrow commodity category. These insights were recently confirmed empirically by Ha et al. (Soo Junga Ha et al., 2009). Figure 4‐4. Regional Demand
CES function associated with esubd
CES function associated with esubm
QQ (total domestic demand)
QM (composite imports)
QD (domestic output sold domestically)
QMRFT (Rest of World imports)
QMRDT (Rest of U.S. imports)
Thus regional elasticities of substitution for imports should be lower than national elasticities. Esubm, the elasticity of substitution for imports from the rest of the United States and the rest of the world, was set at 1.5 for all sectors except nontradables. Esubd, the elasticity of substitution for the composite of imports from RUS and ROW with domestic production was specified at an even more inelastic value of 0.9. 4.3.6 Investment Quantities of final goods purchased for investment purposes are exogenous (held constant at the baseline amounts), as are the amounts that the investment sector
149 pays out to institutions. Household savings adjust to cover changing prices of investment goods. This is an “investment driven closure.” 4.3.7 Other Closures Labor, capital and water. In the first scenario, total raw water is available in fixed amounts and may not be traded amongst the self‐supplying sectors. In the remaining simulations, total raw water availability is fixed but raw water is mobile between sectors. In Nevada, this is roughly realistic over the medium term in that agricultural water rights can be purchased by municipalities or other interests, although water transfers may sometimes be subject to judicial review. A marketplace for water rights is assumed to roughly approximate a competitive market despite some restrictions on sales. Labor is mobile between sectors and unemployment is endogenous. Capital is fixed and sector specific. This closure for a regional economy is an intermediate length run. A long run closure for a region would allow labor mobility between regions as well as capital mobility between sectors and regions (Holland, 2010). Foreign and domestic current and capital accounts. The exchange rate is held constant while the trade balance adjusts. Savings and investment closure. Savings levels are endogenously determined to finance an exogenous mix and level of investment goods, at endogenous prices.
150 4.4 Data and the integration of virtual water and water footprint analysis. 4.4.1 Money data. Inter‐industry dollar transactions and trade data are from the IMPLAN database for Nevada (Minnesota IMPLAN Group, 2010). The 440 industry sectors in IMPLAN have been aggregated into the 20 industry sectors in the CGE model. The data from IMPLAN was customized in the following ways: 1. Most of the treated water commodity sold by water utilities was recorded as being produced by the state and local government sector in the IMPLAN data. For this CGE model, all of the treated water commodity is produced by the water utility sector. The “make” table has been adjusted accordingly for the state and local government enterprises sector. 2. The local (Nevada) use of hay was set so that all Nevada hay demand is satisfied first by Nevada supply. The general rationale for the aggregation of the 440 IMPLAN industry sectors to the 20 in the CGE model was to combine sectors that have similar water use intensity. Nevada’s agricultural sectors, electric utilities, mining and the water and sewer utilities sector remain disaggregated so to enable the modeling of untreated water as a factor of production for those sectors. 4.4.2 Water data. Virtual water flows as defined by Hoekstra measure water consumption, i.e. water that evaporates or is incorporated into the product and leaves the region
151 (Hoekstra et al., 2009). In contrast, water flow data used in this analysis represents water withdrawals and includes all water diverted to the sector’s use, whether water returned to the Nevada region or not. That is, the water intensity factors measure water withdrawn, not water consumed. Water withdrawal coefficients were found using a variant of the Blackhurst et al. methodology (2010). United States Geological Survey national level water use data is combined with the irrigation survey from NASS and several other sources to find national water use coefficients (National Agricultural Statistics Service, 2008, United States Geological Survey, 2010). Blackhurst’s approach was adapted for use at the state level with IMPLAN data. More detail on this method and adaptation are in Appendix A. Further adaptations of the method were made so that the method is internally consistent within the water‐CGE model in two ways. First, treated water use amongst sectors is distributed using an assumption of a single price paid for the commodity supplied by the water utility sector, and purchases from the water utility by the twenty sectors in the base social accounting model for Nevada are derived from the IMPLAN data described above. Secondly, water use intensities by sector for the simulation are calculated directly from quantities of raw and treated water use implied endogenously by the model. Because labor and capital can be substituted for raw water, and because imported virtual water can be substituted for regional direct or indirect water use, the water intensity factors are thus endogenous and will potentially differ among simulations.
152 4.5
Virtual water flow and footprint calculations. The framework for the environmental input output method used to calculate
virtual water content is described in Dietzenbacher and Velazquez (2007). This method, as adapted, uses a separate vector of water intensities by sector, given in terms of direct water use in acre‐feet per unit of output in combination with an industry by industry matrix of input output coefficients. Several adaptations have been made to the method so that it can be used with IMPLAN SAM data that contains a use and make matrix, and so that a footprint can be found with the SAM data after a simulation in which relative prices have all changed. Following Dietzenbacher and Velazquez and adapted to the Nevada footprint model, the elements of the input‐output framework are as follows:
D, a 20 by 20 market shares form of the make matrix
B, a 20 by 20 regional use matrix
An 20 by 20 total requirements matrix, (I‐DB)‐1 formed using the industry technology assumption31
An 20 by 8 matrix of regional final demands, F. Final demands include households, government sectors and investment, and foreign and domestic exports
An 20 by 1 vector of outputs, x
V’, a 4 by 20 vector of value added
31
The industry based assumption is that all commodities produced by an industry have the same input structure. See Miller and Blair, p. 192.Miller, Ronald E. and Pater D. Blair. 2009. Input‐Output Analysis: Foundations and Extensions Cambridge, UK: Cambridge University Press.
153
M, a two by 20 matrix of imports
EX, a 20 by two matrix of exports
The additional information needed to determine virtual water content is contained in γ, an 28 by 1 vector of water use per industry sector and institutional sector. The base levels of raw water use are found using the modified Blackhurst methodology. Treated water use is estimated using an assumption of one price for the water utility sector commodity. After simulation, the quantity of both treated and raw water use is an output of the CGE model and is what is used to calculate the new γ. All data except for total water use are from the 2010 Nevada IMPLAN model aggregated to 20 sectors. Also, all final demand and import data is converted to an industry basis by pre‐multiplying by the D matrix. I find a parallel for the Leontief matrix, L* , using the industry based technology assumption, and as usual we have: ∗
∗
∗
where f tot is the sum of all final demands across institutions. Output multipliers
are:
i ∗ L* Output multipliers give us the additional output needed to produce an additional unit of final demand in a sector. We would like to find γ*, the vector of “virtual water” multipliers that gives us the additional water that is needed to produce an additional unit of final demand in sector j, both directly and indirectly. In typical fashion: ∗
′ ∗ L* .
154 For simulation footprints a new water intensity vector is found in the same way, but price ratio adjustment for exports and imports is carried out since the ratios are no longer one to one. 4.6
Simulations The baseline social accounting matrix (SAM) data represents the state of Nevada
in 2010. Five different simulations were compared to the baseline CGE solution (that replicates the baseline SAM): Scenario 1: There are water delivery shortages of 20%. Raw water is not mobile between sectors. Scenario 2: There are water delivery shortages of 20%. Raw water is mobile between sectors. Scenario 3: There are water delivery shortages of 20%. In addition, world food price of both imports and exports increases 10%. Scenario 4: Scenario 3 with ‘Arid Country’ policy that encourages agricultural imports with an import subsidy of 10%. Scenario 5: Scenario 3 with ‘Local Food’ policy that encourages homegrown agricultural products and discourages imports with an import tariff of 10%. To simulate conditions of global warming, a 20% shortage of water deliveries was stipulated. According to USGS estimates, 2005 water deliveries in Nevada were approximately 2.7 million AF. This is the fixed supply in the base model. In all five
155 scenarios, a 20% reduction to 2.1 million acre‐feet is the fixed supply available to the economy. In the first scenario, water is not mobile between sectors, that is, there is no water market. Each sector receives a 20% reduction from its baseline use. In the second scenario, raw water is mobile between sectors. In the third through fifth scenarios, an exogenous increase in world prices for agricultural and food products is introduced. Clearly, world food prices would likely have a large influence on agricultural and food prices locally. If conditions of global warming, energy price increases, larger populations or decreasing fossil groundwater supplies tighten food supplies world‐wide, price increases may present a summary of these more difficult conditions. This explores the problem many national or regional water‐CGE models have. Often, analysis shows that water should be removed from agricultural uses and put into more profitable sectors. However, this advice could differ if world‐ wide the same advice removes land and water from agricultural activities and prices of agricultural goods rise. Despite the existence of a water market that allows for it in this model, it is not typically easy to reverse sales of water rights to non‐agricultural sectors. The third scenario combines the 20% water shortage with 10% world price increases in all agricultural products as well as in the food processing sector. In the fourth scenario, state policy is to encourage imports of agricultural and food products with a 10% subsidy, approximating the policy of some arid Middle Eastern countries. In the fifth scenario, state policy is to discourage imports with a tariff so as to encourage local food production. Subsidies are paid for by the state government which must
156 reduce its other normal spending as a result. The opposite happens for the fifth scenario. Tariffs are collected by state government and increase its normal spending. 4.7
Results Baseline results show Nevada is a net importer of virtual water, importing more
than 10% of its total water footprint of about 3,000,000 AF (Table 4‐2). Trade volume of virtual water is large for Nevada. Virtual water exports are surprisingly large for such an arid state with virtual water flow embodied in exports of about 1,650,000 AF or more than 60% of total water withdrawals. Virtual water imports are also large at about 2,000,000 AF. Baseline prices for raw water rental are $54 per acre foot. This value was derived by using land values for isolated Nevada ranches with a given amount of water rights with the assumption that land prices in this situation derive most of their value from water rights. For more detail about this method see the third essay in this collection. Scenario 1 and 2 compare the resulting economy and water use under a condition of 20% water shortage for the situation where water is mobile across sectors and where it is not mobile across sectors. As one might expect, the flexibility enabled by allowing inter‐sector trades ‐‐adding a water market ‐‐ softens the negative impacts of a water shortage. Water is allowed to flow to its highest use. The price of water when it can be traded among sectors rises to $69, an increase of 27% over the baseline price. In contrast, the average rental rate of an acre‐foot of water when water cannot be traded goes up 460%, driven largely by the inflexible demand of the water utility sector.
157 Table 4‐2 Virtual Water Trade Flows Simulation Description 0. Nevada 2010 Baseline 1. 20% decrease in water supply with no water market 2. 20% decrease in water supply with water market 3. 20% decrease in water supply with world food price increases of 10% 4. Scenario 3 with ag import subsidy "Arid Country" 5. Scenario 3 with ag import tariff "Local Food"
Regional Total water Net virtual direct water footprint Water Rent water (AF) use (AF) imports (AF) 414,702
2,663,153
3,077,855
$ 54
482,964
2,138,403
2,621,367
$302
559,816
2,138,897
2,698,712
$ 69
399,978
2,138,878
2,538,856
$ 93
459,117
2,138,883
2,598,000
$100
350,465
2,138,872
2,489,338
$ 86
Postulating the existence of a fully integrated water market in this way abstracts from the fact that the state of Nevada is large, and substantial transportation costs would be incurred to move water among sectors (in most cases). In addition, the current model specification ignores environmental externalities that can occur when water is moved from its basin of origin. Postulating the total immobility of water between uses is also an extreme abstraction. However, the simulated large price increase caused by the water shortage may accurately predict the value of urban water under conditions of shortage. Furthermore, the magnitude of the price increase may provide an estimate of the additional transportation costs that could be rationalized under water shortage conditions. For example, this kind of simulation may estimate the economic feasibility of the controversial 300 mile pipeline, now in the planning stages, that would move water from White Pine and Lincoln Counties, southward to Nevada’s largest urban population in Las Vegas.
158 Regardless of the assumption about a water market, the Nevada water footprint shrinks when there is a water shortage, but by less than the amount of the water shortage, because virtual water imports increase to make up for some of the loss. There are somewhat larger net virtual water imports assuming the existence of a market than without one, perhaps because market rationalization diverts water away from agricultural exports (see table 4‐3). Neglecting environmental externalities, the scenario that includes a water market leads to superior economic outcomes despite the water shortage, with no employment or household income lost. Table 4‐3 Virtual Water Trade Flows, Percent Change from Baseline Simulation Description 0. Nevada 2010 Baseline 1. 20% decrease in water supply with no water market 2. 20% decrease in water supply with water market 3. 20% decrease in water supply with world food price increases of 10% 4. Scenario 3 with ag import subsidy "Arid Country" 5. Scenario 3 with ag import tariff "Local Food"
Net virtual Regional Total water Water water direct water footprint Rent imports (AF) use (AF) (AF) 0%
0%
0%
0%
16%
‐20%
‐15%
459%
35%
‐20%
‐12%
27%
‐4%
‐20%
‐18%
71%
11%
‐20%
‐16%
85%
‐15%
‐20%
‐19%
59%
A market is assumed to exist for scenarios 3, 4 and 5, in all cases water is mobile among all sectors. Scenarios 3, 4 and 5 simulate world food prices rising 10% for both imports and exports ‐‐ possibly due to climate change or population pressures. As shown by the results in Table 4‐4, even with a water market in place, an overall rise in
159 world food prices puts a much larger burden on the Nevada economy than a 20% water shortage. Employment falls over 1% and household income falls over 2%. Table 4‐4. General Economic Indicators, Percent Change from Baseline General economic indicators 0. Baseline scenario 1. 20% decrease in water supply with no water market 2. 20% decrease in water supply with water market 3. 20% decrease in water supply with world food price increases of 10% 4. Scenario 3 with ag import subsidy "Arid Country" 5. Scenario 3 with ag import tariff "Local Food"
0.0%
Household Income 0.0%
‐1.0%
‐1.6%
‐11.1%
‐0.2%
0.0%
0.0%
1.2%
‐0.3%
‐1.1%
‐2.4%
‐14.1%
‐0.3%
‐0.1%
0.4%
3.8%
‐1.6%
‐2.1%
‐5.2%
‐31.9%
0.8%
Employment
Trade Govt. Balance Consumption 0.0% 0.0%
Water rents rise over 70%, to $93 an acre‐foot (Tables 4‐2 and 4‐3) in Scenario 3. However, the increase in agricultural import prices leads to less quantity demanded, while the increase in export price (export demand) does not lead to a comparable increase in agricultural exports to offset the effect on Nevada’s trade balance. Net imports and Nevada water footprint decrease 4% and 18% respectively. Finally, scenarios 4 and 5 explore the Arid Country and Local Food coping strategies. Arid country policy encourages imports of water intensive commodities with a subsidy. Note that while the 10% subsidy on food and imports reduces the import price to the level in Scenario 2, food and agricultural commodity export prices continue to be 10% higher than in Scenario 2. Furthermore, the state and local government must pay for the subsidies which crowds out state and local consumption. In terms of
160 Table 4‐5. Percent Change compared to the Baseline in the Agricultural, Water Utility and Food Processing Sectors Scenario sector Hay Dairy Livestock Vegetables & melons Other ag Water utility Food processing Hay Dairy Livestock Vegetables & melons Other ag Water utility Food processing Hay Dairy Livestock Vegetables & melons Other ag Water utility Food processing Hay Dairy Livestock Vegetables & melons Other ag Water utility Food processing
1
2 3 4 5 Price paid for locally produced commodities (PD) 8.5% 13.7% 21.9% 24.6% 19.4% 3.6% 7.3% 13.4% 10.1% 16.8% 1.9% 1.8% 18.5% 18.7% 18.0% 0.0% 0.4% 4.4% 5.0% 3.8% 1.8% 2.4% 7.9% 7.0% 8.7% 66% 1% 2% 3% 1% 0.0% 0.2% 4.2% ‐0.5% 9.2% Commodity Imports (QM) 2.2% 3.4% 6.6% 23.1% ‐6.8% ‐3.9% ‐5.1% ‐5.0% 4.9% ‐13.6% 0.1% 0.0% 14.7% 27.1% 3.7% ‐1.4% 0.0% ‐7.5% 3.3% ‐16.6% ‐2.4% ‐2.5% ‐2.0% 4.9% ‐8.0% 0.0% 0.0% 0.0% 0.0% 0.0% ‐0.6% ‐0.1% ‐2.1% 1.7% ‐5.3% Commodity Exports (QE) ‐22.6% ‐33.1% ‐24.8% ‐26.6% ‐23.2% ‐14.7% ‐25.3% ‐14.3% ‐5.0% ‐23.3% ‐6.1% ‐5.9% ‐10.9% ‐10.6% ‐11.0% ‐1.3% ‐1.4% 10.4% 9.9% 10.9% ‐8.1% ‐10.1% 4.5% 4.8% 4.3% ‐37.7% ‐1.2% 1.0% 1.6% 0.2% ‐0.6% ‐0.8% 17.5% 30.1% 5.6% Local Final Demands (QD) ‐5.1% ‐7.8% ‐2.8% 0.1% ‐5.7% ‐6.9% ‐10.9% ‐7.6% ‐4.7% ‐10.8% ‐1.6% ‐1.6% 7.3% 8.0% 6.1% ‐1.4% ‐0.4% ‐3.1% ‐2.0% ‐4.2% ‐4.0% ‐4.5% ‐0.3% ‐2.2% 1.3% ‐19.7% ‐0.7% ‐2.8% ‐1.6% ‐4.0% ‐0.6% ‐0.3% 2.7% 1.2% 3.8%
economic indicators, the arid country strategy appears to be surprisingly successful, especially in comparison to either doing nothing (scenario 3) or the “Local Food” policy. Employment loss is minimal, household income rises slightly over the baseline condition, and exports increase. As expected, the subsidy encourages an increase in the
161 import of agricultural goods. Concurrently, net virtual water imports increase, and the overall water footprint does not decrease as much as it does the ‘do nothing’ scenario 3 or the ‘local food’ scenario 5. In the local food scenario 5, the state/local government imposes a “tariff” on imports of agricultural goods in hopes of encouraging local production of these products. The strategy is meant to promote self‐sufficiency and to enhance both regional welfare and environmental welfare. The simulation results indicate that the strategy is a failure economically. The ‘local food policy’ results in the largest decrease in employment of all the scenarios, and a very large decrease in household income. Only two food producing sectors increase production compared to the other two scenarios: the other agriculture sector and the food processing sector. However, the strategy does succeed in reducing imports of water intensive products and reducing Nevada’s water footprint, possibly helping to reduce some environmental externalities. 4.8
Conclusion By constructing and applying a CGE model that operationalizes the concepts of
virtual water and water footprints I have been able to explore both the economic consequences of the arid country and local food policy as well as to account for water use inside and outside of the state of Nevada, under varying conditions and policy strategies. One result is that where mobility of water between sectors is allowed, the increased flexibility greatly reduces the economic hardship imposed by a water shortage. Without such a market, water shortages will decrease household income and increase unemployment. However, when there is a market for water, overall water
162 withdrawals increase, which may also increase environmental externalities. I also find that compared to a large decrease in water availability, a large increase in world food prices causes relatively more economic disruption. Many regional water‐CGE studies of trade‐offs between urban and agricultural water use conclude that rationalization of water use should lead to less water use in agricultural production and more in other types of industries and final consumption. However, if the long‐term world outlook is for higher food prices, this policy advice may need to be tempered. It is important to consider how the context of the regional model might change under global warming, population increases, shortages of groundwater and the like. For agricultural goods, these changes may be easy to simulate in a CGE model as an increase in world food prices. Such changes could be even more important than local changes in water resources. I compared two competing and opposite policies with a do nothing option under a severe global warming scenario: the “arid country” policy and the “local food” policy. In this highly stylized model, the arid country policy produced a far better economic outcome, but the local food policy succeeded in reducing the use of imported water resources. Using a CGE model in connection with the water footprint allowed a fuller exploration of both the economic consequences and water resource use consequences of these policies than is possible with a CGE model analysis alone. This work is but a preliminary investigation of these issues. Costly transportation and infrastructure would be necessary for a water market that allowed the full mobility of water among sectors in a region as large as Nevada. The present model abstracted
163 entirely from the cost of moving water. The present model simulation assuming no water market at all may, however, indicate how much urban interests might be willing to pay for water transport if there were a water shortage. A “local food” policy may be a strategy that unfolds over a long time period, which would allow for the accompanying adjustment of human capital and infrastructure to suit it. These longer term capital adjustments are not modeled in this paper. Also, local food advocates are concerned with multiple environmental and health concerns that cannot all be incorporated into a CGE model. This preliminary analysis can in any case show that a tariff policy to encourage local production may not be successful in meeting the economic goals of a local food movement. 4.9 References Alexander, Patty; Levi Brekke; Gary Davis; Subhrendu Gengopadhyay and Katrina Grantz. 2011. "Secure Water Act Section 9503c ‐ Reclamation Climate Change and Water 2011," U.S. Department of the Interior. Denver, CO: Policy and Administration Bureau of Reclamation. Berck, Peter; E. Golan and B. Smith. 1996. "Dynamic Revenue Analysis for California" California Department of Finance. Sacramento, CA. Berrittella, M.; A. Y. Hoekstra; K. Rehdanz; R. Roson and R. S. J. Tol. 2007. "The Economic Impact of Restricted Water Supply: A Computable General Equilibrium Analysis." Water Research, 41(8). Berrittella, M.; K. Rehdanz; R. Roson and R. S. J. Tol. 2008. "The Economic Impact of Water Taxes: A Computable General Equilibrium Analysis with an International Data Set." Water Policy, 10(3). Bureau of Reclamation. 2003. "Water 2025: Preventing Crises and Conflict in the West." Calzadilla, Alvaro; Katrin Rehdanz and Richard S.J. Tol. 2011. "Water Scarcity and the Impact of Improved Irrigation Management: A Computable General Equilibrium Analysis." Agricultural Economics, 42, pp. 305‐23.
164 Castellano, Ignacio Cazcarro; Rosa Duarte Pac; Julio Sanchez Choliz and Cristina Sarasa Fernandez. 2010. "Water Rates and Responsibilities of Direct, Indirect and End‐Users in Spain" In 18th International Input‐Output Conference. Sydney, Australia: International Input‐Output Association. Chapagain, A. K.; A. Y. Hoekstra; H. H. G. Savenije and R. Gautam. 2006. "The Water Footprint of Cotton Consumption: An Assessment of the Impact of Worldwide Consumption of Cotton Products on the Water Resources in the Cotton Producing Countries." Ecological Economics, 60(1), pp. 186‐203. Dietzenbacher, Erik and Esther Velazquez. 2007. "Analysing Andalusian Virtual Water Trade in an Input‐Output Framework." Regional Studies, 41(2), pp. 185‐96. Fadali, Elizabeth; Kim Rollins and Shawn Stoddard. 2012. "Determining Water Values with Computable General Equilibrium Models," In The Importance of Water to the U.S. Economy: Technical Workshop, ed. Industrial Economics Inc. Cambridge, MA. Frontier Economics. 2008. "The Concept of 'Virtual Water' ‐ a Critical Review," Melbourne: Victorian Department of Primary Industries. Ha, Soo Junga; Karen Turner and Geoffrey Hewings. 2009. "Econometric Estimation of Armington Import Elasticities for Regional CGE Models of the Chicago and Illinois Economies," In Scottish Institute for Research i Economics. Edinburgh, Scotland: University of Strathclyde. Hoekstra, A. Y. 2009. "Human Appropriation of Natural Capital: A Comparison of Ecological Footprint and Water Footprint Analysis." Ecological Economics, 68(7), pp. 1963‐74. Hoekstra, A. Y. (ed.). 2003. "Virtual Water Trade: Proceedings of the International Expert Meeting on Virtual Water Trade," In Value of Water Research Report Series No. 12, ed. A. Y. Hoekstra. Delft, The Netherlands: UNESCO‐IHE Delft. Hoekstra, A. Y.; A. K. Chapagain; M. M. Aldaya and M M. Mekonnen. 2009. "Water Footprint Manual: State of the Art 2009," Enshade, Netherlands: Water Footprint Network. Hoekstra, Arjen Y. and Ashok K. Chapagain. 2007. "The Water Footprints of Morocco and the Netherlands: Global Water Use as a Result of Domestic Consumption of Agricultural Commodities." Ecological Economics, 64(1), pp. 143‐51.
165 Holland, David. 2010. "What Happens When Exports Expand ‐‐ Some Ideas for Closure of Regional Computable General Equilibrium Models." Annals of Regional Science, 45(3), pp. 439‐51. Kumar, M. Dinesh and O. P. Singh. 2005. "Virtual Water in Global Food and Water Policy Making: Is There a Need for Rethinking?" Water Resources Management, 19(6). Lahiri, Supriya; Mustafa Babiker and Richard S. Eckaus. 2000. "The Effects of Changing Consumption Patterns on the Costs of Emission Restrictions," In MIT Joint Program on the Science and Policy of Global Change. Cambridge, MA: MIT. Lluch, Constantino; Alan A. Powell and Ross A. Williams. 1977. Patterns in Household Demand and Saving. Washington, D.C.: Oxford University Press. Lofgren, Hans; Rebecca Lee Harris and Sherman Robinson. 2002. "A Standard Computable General Equilibrium (CGE) Model in GAMS," Washington, D.C.: International Food Policy Research Institute. Masi, Brad ; Leslie Schaller and Michael H. Shuman. 2010. "The 25% Shift: The Benefits of Food Localization for Northeast Ohio & How to Realize Them," In. Cleveland, OH: Cleveland Foundation, ParkWorks, Kent State University Cleveland Urban Design Collaborative, Neighborhood Progress Inc., Cleveland‐Cuyahoga County Food Policy Coalition. McChesney, John. 2011. "Nevada Water Maven: “I Would Not Declare the Drought over on the Colorado River”," In John McChesney's Blog. Palo Alto, CA: Stanford University Rural West Initiative Bill Lane Center for the American West. Miller, Ronald E. and Pater D. Blair. 2009. Input‐Output Analysis: Foundations and Extensions Cambridge, UK: Cambridge University Press. National Oceanic and Atmospheric Administration. 2012. "Nevada Precipitation Rankings " National Climatic Data Center. Nevada State Demographer. 2013. "2011 Estimates, Estimates by County, City and Unincorporated Town," Population Estimates. Reno, NV: The Business Services Group ‐ University of Nevada Reno. Reimer, Jeffrey J. 2012. "On the Economics of Virtual Water Trade." Ecological Economics, 75, pp. 135‐39. Roson, Roberto and Martina Sartori. 2010. "Water Scarcity and Virtual Water Trade in the Mediterranean," In Working Paper Department of Economics Ca' Foscari University
166 of Venice, 1‐13. Venice, Italy: Ca' Foscari University of Venice. Stodick, Leroy; David Holland and Stephen Devadoss. 2004. "Documentation for the Idaho‐Washington CGE Model," School of Economic Sciences, Documentation for a GAMS regional computable general equilibrium model built specifically to use IMPLAN data. Pullman, WA: Washington State University. Truckee Meadows Regional Planning Agency. 2008. "Frequently Asked Questions About Sustainable Water Resources and Growth in the Truckee Meadows," Reno. NV. Turner, Karen; Michelle Gilgartin; Peter G. McGregor and J. Kim Swales. 2012. "An Integrated I‐O and CGE Approach to Analysing Changes in Environmental Trade Balances." Papers in Regional Science, 91(1), pp. 161‐80. Turner, Karen; Michelle Gilmartin; Peter G. McGregor and J. Kim Swales. 2009. "The Added Value from Adopting a CGE Approach to Analyse Changes in Environmental Trade Balances," Strathclyde Discussion Papers in Economics. Glasgow, Scotland: University of Strathclyde Department of Economics. Wichelns, Dennis. 2010. "Virtual Water: A Helpful Perspective, but Not a Sufficient Policy Criterion." Water Resource Management, 24, pp. 2203–19. Yang, Hong and Alexander Zehnder. 2007. "`Virtual Water': An Unfolding Concept in Integrated Water Resources Management." Water Resources Research, 43(12).
167
5 5.1
Summary Insights provided by this research
This research provides the following insights about water use, virtual water and water footprints in economic water policy analysis. First, the tracking of physical quantities of water use in conjunction with economic activity is useful. Not including an account of water resources in a model about water policy can lead to mistaken policy advice, since it does not force the modeler to think about and formalize exactly how water and the economy are connected, as can be observed in the two reports on Las Vegas water supply interruptions. However, in modeling water and the economy, actual water withdrawals rather than water consumption are more natural to use since most economic transactions concerning water will be for water withdrawals. Firms and consumers will typically have to make decisions based on prices for withdrawals, and thus incentives will relate to water withdrawals as well. For example, in the water CGE models, money data in the base SAM was for water withdrawals, so the model was specified with respect to withdrawals rather than water consumption. This conflicts with the definition and methodology espoused by the Water Footprint Network, which uses water consumption to measure virtual water content. However, virtual water content and footprints can be found using the data on direct water withdrawals after model results are found. Footprint and virtual water are more useful as key outcome variables reported as descriptive measures at the end of the modeling process.
168 Virtual water measurements help us to see the whole system picture of water use including water embodied in imports and exports. Several situations which require a description of changes in virtual water content or footprint in order to fully analyze water policy are identified: 1. When water use is tracked over time in order to measure water efficiency of an economy, the virtual water content of imports and exports cannot be ignored. This correlates with energy and greenhouse gas modeling where the importing and exporting of final or intermediate goods and services that require large inputs of energy must be taken into account to fully understand the consequences of energy policy changes. 2. Water subsidies (or taxes) may have an incidence on both local regional populations and populations outside of the region of interest. Virtual water concepts may help analyze this subsidy incidence. 3. Understanding the sustainability implications of a given water, environmental or economic policy both inside of and outside of a region may require footprint analysis, which can answer questions about whether water‐based externalities are being exported to other regions. Nevada is found to be a net importer of virtual water. This is true even though water is not freely allocated nor market‐priced in the State of Nevada. There is a tendency to import goods that intensively employ the state’s relatively scarce factor of production (water) and to export things that are less water‐intensive, as predicted by trade theory (which also assumes a market for water).
169 CGE models that operationalize water footprint and virtual water use constructs can be prepared from existing databases and existing canonical CGE model structures. The necessary adaptations and augmentations are described in three of the essays in this dissertation, particularly the last essay. For this essay a regional water‐CGE model is used in conjunction with endogenous water intensity coefficients to calculate water footprints for the first time in a U.S. context. In order to find water footprints, water accounts data are needed. This research points out a way of finding water accounts data from USGS water use data available for each state and county in the U.S. every five years. Although, the method is crude, it provides a starting place for full water accounts data and can be used for any other state or county. In the third essay, one method used by many water CGE modelers to include water as a factor of production by estimating the rents due to it is clarified and a sensitivity analysis indicates that care in choosing these values is warranted. Many water CGE models that consider urban and rural water use trade‐offs point out the great advantages of allowing for water markets between sectors, thereby increasing flexibility and softening the hardships of water shortages. Often, water use is predicted to profitably shift to urban sectors out of agriculture. The irreversible nature of such a change in water use is typically ignored. The fourth essay provides a simple way of exploring a risky future. It is easy to model exogenous increases in food prices in a CGE model. These food price increases are one way of summarizing risks to food supplies due to global warming, depletion of fossil groundwater supply and increasing population where irreversible water reallocation is occurring.
170 Water footprint measurements in and of themselves will usually be inadequate for policy analysis since the footprint focuses on only one factor of production, can give no indication of the opportunity cost and the endowments of water in a given time and place, and does not take into account different production technologies and the relationship between water and all other inputs to production, amongst other failings. In particular, an input‐output framework is inadequate for policy analysis since it typically assumes no constraint on input factors, including water, and economic actors do not respond to price signals. But the use of a water‐CGE model in conjunction with the virtual water content and water footprint concepts can address most of these problems. Finally, this research has identified some important directions for future research. One significant shortcoming of existing water‐CGE models (including the models created for these essays) is the assumption of costless mobility of water among alternative uses. We need to account for all costs of moving water between users in order to realistically estimate the benefits of opening water markets or the economic benefits/costs of inter‐basin water transfer projects, for example. Water accounts data in general needs further development, for example, in the separation of the sewer and water utilities, and in understanding different prices paid by different industries. A truly multi‐regional model with separate water accounts data for all regions would help provide more realistic calculations of virtual water content in imports. Water utilities and water districts are government or quasi‐government organizations in Nevada so the water utility sector could be modeled to reflect that rather than assuming that it
171 operates as a firm optimizing profits in a competitive marketplace. Also, subsidies are suspected to exist in the water factor and water utility sectors so they should be explicitly modeled. Price responsiveness to treated water should be incorporated into the production functions of the industry sectors. In sum, there remain many measurement and modeling challenges to be addressed in future economic models concerning human water use. These four essays have provided a basic point of departure.
172
6
Appendix A. Water Accounts
Adaptation of Blackhurst et al. method for obtaining industry sector water intensity factors Blackhurst et al. (2010a; 2010b) develop a method to calculate water withdrawal intensity factors by industry sector at a national level. I have adapted their method for use at a state level, for both withdrawals and consumption, and for use with state‐level IMPLAN data. I have used the method to calculate water intensity factors for all 440 industry sectors in IMPLAN for the state of Nevada in a manner that can be duplicated for any other state. The process can also be used at the county level if adaptations are made to the process that is used to derive the crop irrigation water intensity factors.
Blackhurst et al. use USGS national level water use data in combination with the irrigation survey from NASS and several other sources to find national water use coefficients. USGS water use data by major sector by state and by county is produced every five years (Kenny et al 2009). The most current water use data available from the USGS is for 2005, while the data for the IMPLAN social accounting matrix that I use in the CGE model is for 2010. After examining total water use data from the major utilities in Nevada, it was determined that urban water demand did not increase over the period. It was assumed that most self‐supplied industry water use, which is tied to limited water rights, has not changed substantially over the period from 2005 to 2010.32 Thus no upward adjustment in water use was made to the 2005 USGS data.
Withdrawal versus Consumption
Water consumption is defined as water that leaves the region of interest through evaporation, transpiration, or because it is contained within trade goods. Consumption does not include water returned to the regional water supply through, for example, sewer systems and water treatment facilities, run‐off, or seepage into groundwater. Water withdrawals are actual water volumes used at a site, for treated water, and water volume used at a source for supplied water.
Smith et al. (2011) adjust water withdrawals to water consumption using state‐ specific values. Using the USGS estimates given in Smith et al. for state and category (domestic supply, industrial and commercial, irrigation, etc.), consumption rates are first applied to the USGS categories of water use; the Blackhurst method is then used with these totals. Table 10 shows how water consumption totals are estimated from water withdrawal data for Nevada. Overall, the USGS estimated that 2.7 million acre‐feet (AF) of water was withdrawn for human use in 2005. Of this, about 50%, or 1.4 million AF, was evaporated, transpired, or otherwise taken out of the region. Thermoelectric plants 32
Mining activity increased substantially over this time period. The ramifications of this for water use have not yet been investigated.
173 are estimated to have the highest rate of consumption out of all Nevada sectors, with 75% of the withdrawn water consumed, whereas industrial and commercial uses are estimated to consume 15% of the water withdrawn for their use. For the current application both water consumption and water withdrawals were used. The USGS estimates for proportion consumed may be used to allow for return flows if desirable. Ideally, particularly for urban water, sewer water returns should be tracked separately, since waste water is an input into costly water treatment and legal requirements for water quality can be a constraint on total water use.
Table 6‐1. 2005 Nevada Water Withdrawals Adjusted to Water Consumption Category Public Supply Domestic Industrial and commercial Self‐supplied Domestic Self Supply Industrial self‐supplied Irrigation Livestock/Aquaculture use Mining Total thermoelectric Total withdrawals, in AF/Yr
Water withdrawals Percent consumed or (AF)* evaporated**
Estimate of water consumption (AF)
472,085 285,446
25.6% 15.0%
120,789 42,817
41,871 6,609 1,678,211 26,715 110,961 41,255 2,663,153
25.6% 15.0% 66.7% 37.0% 27.3% 75.7% 51.3%
10,713 991 1,118,807 9,890 30,262 31,220 1,365,490
Sources: *(Kenny et al 2009), **(Smith et al 2011), author’s calculations
Public Supply
USGS public supply for the state of Nevada is allocated across industry sectors and final demand using purchases of the water, sewage and other systems utility industry in the IMPLAN social accounting matrix. This requires assumptions such as a single price for water across sectors, negligible or equal percentages of the purchases spent on sewer by each industry sector and final demand institution, and national level estimates that fit local production functions. This method needs further testing. For Nevada, it was found that top water users, such as the casino industry, were correctly estimated to be top water users with this methodology. Intuitive results for different income levels of households were also obtained. One puzzle was the very low water use by government institutions and sectors. For example, public schools appear to be a major water user with large areas of irrigated landscape, but water use for the local and state education final demand sector is near zero in the IMPLAN data.
Self‐Supplied water use in power generation, agriculture, manufacturing and mining
USGS estimates for self‐supplied water use are given for the power generation, agricultural and mining sectors. The Blackhurst methodology allocates these estimates
174 to subsectors. If the above method showed water purchases from the treated water utility for these sectors, the two amounts are added together for intensity factors.
Power generation
Self‐supplied withdrawals for power generation can be directly applied to the power generation sector (in our case, sectors 31, 428, federal electric utilities, and 431, state and local electric utilities, in IMPLAN). The withdrawals are spread across the three sectors proportionally to output. Irrigation withdrawal allocations for agriculture Irrigation water for agricultural production represents the vast majority of total water consumption and water withdrawals in Nevada (Table 10). Irrigation for agriculture is the second largest withdrawal after thermo‐electric cooling and is the largest proportion of water consumption nationwide. The Blackhurst et al. method uses the NASS 2008 Farm and Ranch Irrigation Survey (United States Department of Agriculture 2009) combined with Census of Agriculture data on crop acreage (National Agricultural Statistics Service 2009) to allocate USGS estimated water withdrawals to agricultural sectors. The survey contains data on irrigated acreage by crop and average acre‐feet applied by crop for each state. The irrigation survey does not contain county‐level estimates of crop irrigation withdrawals. However, the USGS does give a county‐level estimate of total crop irrigation water applied, and the Census of Agriculture gives crop acreage data by county. Using state‐level water intensity levels with crop acreages, followed by raking total irrigation water use back to USGS as a control, is one method of approximating new water intensity factors for agriculture at the county level. Other, more sophisticated methodologies have been developed for estimation of water consumption by major crop, using weather station data and reference crop evapotranspiration rates as described in Mubako (2011). Some models include rainfall as part of the agricultural sector specification. Rainfall variability is an important driver of irrigation water demand. The Nevada model does not address rainfall issues, since it is a small proportion of total water needed and used. For states with a larger amount of rainfall, this would be an important model consideration. In addition, where it is important to have virtual water measurements that are in sync with the methodology espoused by the Water Footprint Network it would also be important to account for this so‐called green water use (Hoekstra, et al., 2009).
175 Manufacturing
Statistics Canada has developed values for water withdrawal and water consumption per employee for some manufacturing industries by NAICS code (Statistics Canada, 2010). Blackhurst et al. use these values to help allocate industry self‐supplied water across manufacturing sectors. This method has been adapted for use with IMPLAN sectors. The industries most likely to use self‐supplied water are the largest water using manufacturing sectors: food processing, petroleum refineries, coal, metal refining, paper and wood processing and computers and electronics manufacturing. None of these types of manufacturing are prominent in Nevada, which makes these estimates of negligible importance for our model. Other states with more activity in these manufacturing sectors would need to examine the Canadian values more closely.
Mining
Blackhurst et al. use a variety of source materials to determine per employee water use for the mining sectors, which they then apply, raking back to the USGS national mining water withdrawal total. The water intensity factors that Blackhurst et al. give in terms of water use per dollar of output are applied and raked to the USGS estimates for Nevada or Clark County. Figure 6‐1. Modified Blackhurst et al. Method for Deriving Water Intensity Factors
176
7
Appendix B. CGE Model Description
Base model is the Washington State University regional CGE model and documentation is available at http://www.agribusiness‐mgmt.wsu.edu/Holland_model/ .33 The model has been modified for the water CGE application. Abbreviations CES constant elasticity of substitution CET constant elasticity of transformation ROW rest of the world RUS rest of the United States Sets a activities c commodities f factors (ft is foreign trade and dt is domestic trade) t trade regions h households g governments (fgov is the federal government and sgov the state and local government) gh set of governments and households i instititutions ai activity or institution wt water type List of parameters xedc,t Elasticity of demand for world export function esubpa Elasticity of substitution for factors of production esubdc Elasticity of substitution (Armington) between regional output and imports esubsc Elasticity of transformation between regional production and foreign export esubec Elasticity of transformation between row and rus for exports esubmc Elasticity of substitution (Armington) between row imports and rus imports efacf supply elasticity for capital and labor and water sgovbal ‐ state government budget balance θ c,a ‐ yield of output C per unit of activity A icac,a ‐ quantity of C as intermediate input per unit of activity a wica ‐ quantity of water factor per unit of (water utility sector) 33
Stodick, Leroy; David Holland and Stephen Devadoss. 2004. "Documentation for the Idaho‐Washington Cge Model," In ed. School of Economic Sciences, Documentation for a GAMS regional computable general equilibrium model built specifically to use IMPLAN data. Pullman, WA: Washington State University.
177 wicashare ‐ initial share of outlay to water factor vasharea ‐ initial share of outlay to value added factors vasharew ‐ initial share of outlay to labor and capital factors only ada ‐ production shift parameter adw ‐ production shift parameter for water utility prod fctn δf,a ‐ production function share parameter δw production function share parameter for water utility ρa CES production function exponent aδc Armington commodity composite share parameter for production aq c Armington commodity composite shift parameter aρ c Armington commodity composite exponent sδ c Armington CET composite share parameter for domestic sales sρ c Armington CET composite exponent as c Armington CET composite shift parameter eδ c Armington composite share parameter foreign exports eρc Armington composite exponent for exports ae c Armington composite shift parameter for exports mδ c Armington composite share parameter foreign imports mρ c Armington composite exponent for imports amc Armington composite shift parameter for imports tc c Consumption tax (only paid by households) tbnew(C,A) intermediate demand unit tax rate in simulations tuf(FF,A) unit tax on factor use tq c Sales tax tqs c Sales tax on services not previously taxed tmt, c Import taxes te c,t Export tax rate tba Indirect business tax rate mpsh Marginal propensity to save tyg,h Rate of household income tax trhh,h Interhousehold transfers pwmt, c ROW and RUS import price cwts c weight of commodity C in the cpi wfaf,a wage for factor F in activity A xshift c,t Shift parameter for world export demand function λc,h Subsistence level parameter β c,h Marginal budget share parameter
178 inec,h income elasticity Engelwth Engel aggregation weight Frischh elasticity of marginal utility of total expenditure qg c,g government consumption shryi,f instutional share of factor income List of variables PM c Import price (domestic currency) XRt Exchange rate PWE c,t World export price PE c Export price (domestic currency) PQ c Composite commodity price PD c Domestic price of domestic output PMR t,c Regional price of imported commodities PER c,t Regional price of exported commodities PAaδ Activity price PX c Producer price QQ c Quantity supplied to domestic commodity demanders QM c Quantity of imports QD c Quantity of domestic output sold domestically QMRt,c Regional imports QX c Quantity of domestic output QE c Quantity of exports QAa Activity level QFKLa Quantity of capital and labor aggregate QFf,a Quantity of factor f demanded by activity a QFSf factor f supply QINT c,a Quantity of intermediate use of commodity C by activity A YHh Gross household income NYHh Net household income QH c,h Household consumption DUMMY for objective fctn QERc,t Regional exports PVAa Value added price PVAWa Price for composite of labor and capital WFf Average wage or rental rate of factor f
179 YFi,f Factor income YFG Federal government revenue EFG Federal government expenditure YSG State government revenue ESG State government expenditure QINVc Investment demand QIINVi Investment demand by institutions WALRAS Dummy variable IADJ Investment adjustment variable IIADJ Institutional investment adjustment variable IADJSG1 c Investment equation adjustment variable IADJSG2c Stone‐Geary investment adjustment variable IINCOME Total investment expenditures on capital goods (commodities) OBJ trying to do as lofgren exercise suggests SADJ Savings adjustment variable SGADJ State government spending adjustment variable for quantity purchased WFDISTf,a Wage distortion factor INDTg Total indirect taxes IMAKEQi,c IMake matrix (quantity) SHIFTFFf Factor supply equation shift variable FSAVX Exports foreign savings DSAVX Exports RUS savings CPI Consumer Price Index GDP gdp calculation for maximization objective FGOVBAL federal government surplus or deficit FIMPRTt,f factor imports GTRNSFRh,g govt transfers to households TRNSFRg,g intergovernmental transfers HEXPRTh,,t household receipts from outside of region sources GEXPRTg,,t govt receipts from outside of region sources HIMPRTt,,h household imports GIMPRTt,g govt imports WATER ai,wt type of water use in AF by activity and institution TARIFF collection of tariff revenue or payment of subsidy DUTY collection of duty revenue or payment of subsidy
180 Equations Import price equation (for commodities that are imported) PMR , pwm , 1 tm , Tariff on imports collected by govt. TARIFF
tm , pwm , QMR ,
Regional export price equation (for commodities that are exported) PER , PWE , 1 te , Duty on exports DUTY
te , QER , PWE ,
Armington import composite equation for commodities which have imports QM
am mδ QMR
1
,
mδ QMR
,
ROW‐RUS import ratio QMR QMR
, ,
PMR PMR
, ,
mδ 1 mδ
Absorption equation for one imported commodity (either ROW or RUS imports but not both) QMR , QMR , QM Price for one imported commodity (either ROW or RUS imports but not both) PM PMR , PMR , Import output value PM QM
PMR
,
QMR ,
Armington export composite equation where exports are greater than zero for both foreign and domestic trade
181 QE ae eδ QER ROW‐RUS export ratio QER , QER ,
,
PER , PER ,
1
eδ QMR
,
eδ 1 eδ
Export output value PE QE
PER
,
QER ,
Absorption equation for one exported commodity (either ROW or RUS exports but not both) QER , QER , QE Price for one exported commodity (either ROW or RUS imports but not both) PE PER , PER , Absorption equation PQ QQ 1 tq PM QM 1 tq tqs PD QD Domestic Output Value PD QD PE QE PX QX Activity price equation PA
PX θ ,
Value added price for all sectors except water utility PVA
PA 1
tb
PQ
tbnew
ica
,
,
Value added price minus water factor rents for water utility sector (a is water utility activity) ∑ PQ ica , PVA PA 1 tb WF WFDIST tuf , wica 1 , ) Leontief‐CES Production Functions for all activities except water utility QA
ad vashare
δ , QF ,
182 Water utility production function adw QA δw vasharew δw
QF
QF
,
,
Factor demand equations for all activities except water utility WFDIST , WF
PVA ad vashare
tuf ,
δ , QF ,
δ , QF ,
Factor demand equation for water utility WF WFDIST , PVAW adw vasharew
δ,
QF ,
δw QF ,
Water utility water demand equation QF , Intermediate input demand equation QINT Output function QX
wica ∗ QA
,
ica
,
∗ QA
θ , QA
IMAKEQ ,
Armington commodity composite supply equation QQ
aq aδ QM
1
aδ QD
Import‐Domestic demand ratio QM QD
PD aδ PM 1 aδ
Composite supply for non‐imported commodities QQ QD Output transformation CET equation QX
as sδ QE
1
sδ QD
183 Export‐domestic supply ratio PE 1 sδ PD sδ
QE QD
Output transformation for non‐exported commodities QX QD Factor income YF ,
shry ,
WF WFDIST , QF ,
FIMPRT ,
Household income YH
YF
PX IMAKEQ
,
trh
1
,
GTRNSFR
,
ty
YH
,
QIINV
,
HEXPRT ,
Net household income NYH
YH
∗ mps
1
trh
ty
Household consumption demand β QH , λ,
,
,
YH
,
NYH
1
ty
YH
,
SADJ
HIMPRT ,
∑ λ PQ 1
,
PQ tc
1
ty
,
YH
tc
Investment demand equation QINV
QINVO
Federal government revenue equation YFG
ty YF
,
,
YH
PX IMAKEQ
,
TRNSFR
,
QIINV
Federal government expenditure equation
GEXPRT INDT
,
184 EFG
GTRNSFR
TRNSFR
,
GIMPRT ,
,
PQ qg
State government revenue equation YSG
ty
,
YF
,
YH
PX IMAKEQ
QIINV
PM QM
TRNSFR
PD QD tq
QINT , tbnew
GEXPRT
,
,
INDT
,
PQ QH , tc PD QD tqs
,
QF , tuf ,
DUTY TARIFF State government expenditure equation ESG
GTRNSFR
TRNSFR
,
SGADJ
PQ qg
GIMPRT ,
,
,
State government budget balance YSG ESG sgovbal Federal government account balance YFG EFG FGOVBAL Factor market equation QFS
QF ,
Composite commodity market equation QINT ,
QQ
QH
,
qg
,
SGADJ ∗ qg
,
QINV
GEXPRT
,
FSAVX
ROW current account balance PER
,
QER
,
HEXPRT
,
,
185 PMR
QMR
,
FIMPRT
,
pwm
QMR
,
GIMPRT
,
tm
,
HEXPRT
,
,
HIMPRT
,
,
RUS current account balance PER PMR
QER
,
,
QMR
,
FIMPRT
,
pwm
,
QMR
,
GEXPRT GIMPRT
,
tm
,
,
,
DSAVX HIMPRT
,
Savings investment balance PX IMAKEQ
SADJ
,
FSAVX
mps 1
DSAVX
PQ QINV
ty YF
QINV
,
YH
,
WALRAS
Price normalization equation CPI
1
INDT
tbshr
tc PQ cwts
Indirect tax calculation tb PA QA
Factor supply equation QFS
SHIFTFF WF
FGOVBAL
sgovbal