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Economic Impact of Adopting Silvopasture in Florida: A Computable General Equilibrium Analysis

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ECONOMIC IMPACT OF ADOPTING SILVOPASTURE IN FLORIDA: A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS By TROY THOMAS TIMKO A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2004

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Copyright 2004 by Troy Thomas Timko

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To my parents, Timothy and Patricia Timko; and to my brother and sister, Todd and Tiffany Timko.

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ACKNOWLEDGMENTS I would like to acknowledge the School of Forest Resources and Conservation at the University of Florida for supporting my education and research efforts. I would also like to acknowledge funding support from the United States Department of Agriculture through the Initiative for Future Agriculture and Food Systems. Additionally, I would like to thank my committee members, Dr. Doug Carter and Dr. Richard Kilmer for helping me to understand the economics necessary to conduct my research; and for their guidance in the process of economic research and writing. I give special thanks to my supervising committee chair, Dr. Janaki Alavalapati. His expert knowledge and instruction were crucial in helping me gain the understanding necessary to carry out this project. In addition, his ability to inspire confidence during the many challenging phases of this research project helped me find the skills to overcome the many hurdles that I faced, thereby making this research possible. iv

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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT.......................................................................................................................ix CHAPTER 1 INTRODUCTION........................................................................................................1 Background...................................................................................................................1 Problem Statement........................................................................................................2 Study Objectives...........................................................................................................2 2 RANCHING, RANCHING IMPACTS, AND SILVOPASTURE...............................4 Cattle-ranching and the Cattle Industry........................................................................4 Environmental Impacts of Ranching............................................................................5 Benefits Associated with Silvopasture.........................................................................7 Benefits Mitigating the Impacts of Cattle-ranching..............................................7 Additional Benefits................................................................................................9 Possible Externality Dilemma...............................................................................9 3 LITERATURE REVIEW...........................................................................................11 Introduction.................................................................................................................11 Why Use Computable General Equilibrium Modeling?............................................11 General Equilibrium Modeling...................................................................................11 Introduction.........................................................................................................11 Input-Output Models...........................................................................................12 Social Accounting Matrices................................................................................13 Computable General Equilibrium........................................................................14 Computable General Equilibrium Modeling..............................................................17 Computable General Equilibrium Models for Policy Analysis...........................17 Computable General Equilibrium and the Environment.....................................19 v

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Cost-Benefit Analysis..........................................................................................21 Contribution.........................................................................................................21 4 MODELING METHOD.............................................................................................22 Introduction.................................................................................................................22 Florida Computable General Equilibrium Model Data..............................................22 Model Structure...................................................................................................23 Additional equations....................................................................................26 Model closures.............................................................................................29 Modeling Shocks to the Existing Equilibrium....................................................30 Shocks Modeled..................................................................................................31 Cost-Benefit Analysis..........................................................................................32 5 MODEL RESULTS....................................................................................................35 Simulation Results......................................................................................................35 Wage-flexible Scenario.......................................................................................35 Wage-rigid Scenario............................................................................................41 Cost-Benefit Analysis.................................................................................................45 6 SUMMARY, IMPLICATIONS, AND RESEARCH OPPORTUNITIES.................49 Summary.....................................................................................................................49 Policy Implications.....................................................................................................50 Model Limitations......................................................................................................51 Research Opportunities...............................................................................................54 APPENDIX A MODEL OUTPUT.....................................................................................................55 Wage-flexible Scenario Output..................................................................................55 Wage-rigid Scenario Output.......................................................................................60 B ADJUSTED DATA SET MODEL OUTPUT ONE..................................................65 Wage-flexible Scenario Output..................................................................................65 Wage-fixed Scenario Output......................................................................................69 C ADJUSTED DATA SET MODEL OUTPUT TWO.................................................75 Wage-flexible Scenario Output..................................................................................75 Wage-fixed Scenario Output......................................................................................79 LIST OF REFERENCES...................................................................................................84 BIOGRAPHICAL SKETCH.............................................................................................89 vi

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LIST OF TABLES Table page 4-1. Specification of the five-sector Florida CGE model..................................................27 4-2. Endogenous variables.................................................................................................28 4-3. Exogenous variables...................................................................................................29 4-4. Closures......................................................................................................................30 5-1. Macro-economic impacts of -25% land base and +25% capital costs.......................36 5-2. Commodity market impacts of -25% land base and +25% capital costs...................37 5-3. Factor market impacts of -25% land base and +25% capital costs............................39 5-4. Macro-economic impacts of -25% land base and +25% capital costs.......................41 5-5. Commodity market impacts of -25% land base and +25% capital costs...................42 5-6. Factor market impacts of -25% land base and +25% capital costs............................44 5-7. Estimated costs and benefits of providing silvopasture.............................................46 5-8. Income adjusted changes in WTP estimates...............................................................47 5-9. Income adjusted estimated costs and benefits of silvopasture...................................47 vii

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LIST OF FIGURES Figure page 6-1. Difference in profitability for cattle-ranchers in Florida............................................50 viii

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Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science ECONOMIC IMPACT OF ADOPTING SILVOPASTURE IN FLORIDA: A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS By Troy Thomas Timko December 2004 Chair: Janaki R.R. Alavalapati Major Department: School of Forest Resources and Conservation Silvopasture, a type of ranching operation, combines trees with forage alongside livestock and produces many environmental benefits over traditional ranching. These benefits include carbon sequestration, biodiversity from wildlife habitat improvement, and reduction in pollution runoff. However, policies targeted to further environmentally benign practices often have far-reaching and sometimes unintended economic consequences. It is therefore necessary to analyze the overall impacts of policies influencing silvopasture to provide policy makers with information on how these policies could affect the economy. This research examines the effects of a 25% reduction available land base and a 25% increase in capital costs for cattle-ranchers in Florida. These shocks simulate the adoption of silvopasture by Floridian cattle-ranchers. A computable general equilibrium model was used to estimate the economic impacts of these policiy shocks under wage-flexible and wage-rigid closure scenarios. We examined changes in various demands for ix

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commodities and factors of production for each of the five modeled sectors in response to the 25% reduction available land base and the 25% increase in capital costs. We also examined the impacts of policy shocks on macroeconomic variables (such as aggregate household expenditure, wages and unemployment). In addition, a cost-benefit analysis was conducted by comparing the costs to households in Florida and the benefits they receive from the environmental services provided by silvopasture. The model results showed that there would be a decrease in the economic welfare of households in Florida under both closure scenarios after enforcing the policy shocks. This decrease in welfare was more severe under the wage-rigid scenario in comparison to the wage-flexible scenario. The cattle industry also experienced a moderate contraction as a result of the policy shocks under both closure scenarios. The effects of the policies on the welfare of Floridians changed slightly when the benefits that households perceived for the services provided by silvopasture were included in the overall change in household welfare. This change resulted in households in Florida experiencing a slight increase in welfare under the wage-flexible scenario. However, under the wage rigid-scenario, the model showed that Floridians still experienced a small decrease in overall welfare. x

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CHAPTER 1 INTRODUCTION Background The cattle-ranching industry is an important agricultural enterprise in Florida, and has a significant influence on the states economy. In the past several years, public concern for the environmental consequences of ranching has been increasing. One concern is increase in water pollution due to runoff of nutrients such as phosphorus from pastureland. This increase in phosphorus content causes eutrophication and subsequent damage in the lakes into which the waters drain. In Lake Okeechobee, for example, the phosphorus content has more than doubled, over the past century (Harvey and Havens 1999). Another concern is the production of methane by cattle. Methane is a greenhouse gas and may contribute to global warming 1 through the greenhouse effect. 2 In addition, the public is becoming more aware that there are opportunities available to help reducing some of these impacts. Silvopasture, a form of agroforestry, 3 offers environmental services that can help meet the publics demand for mitigating the environmental impacts of cattle-ranching. However, there will be costs associated with adopting silvopasture and 1 Global Warming is the potential augmentation of the greenhouse effect due to buildup of gasses that trap heat in the atmosphere (Milich 1999). 2 The Greenhouse Effect is the sum of interactions between the heat that is attempting to escape from the earth to space and the molecules of various gasses that trap this heat, reradiating it within the atmosphere, and impeding its loss to space (Milich 1999). 3 Agroforestry is the management of land for the simultaneous production of food, crops, and trees. 1

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2 with providing these services to the public. Our study provides policy makers with more information on what these costs would be, if ranchers in Florida adopt silvopasture. Problem Statement Previous research suggests that households in Florida prefer to have the above mentioned environmental services associated with the adoption of silvopasture (chapter 2) by ranchers. However, most of the environmental benefits provided by silvopasture are external to ranchers. In contrast, ranchers directly experience the costs of altering their ranching operations. Before making policy decisions regarding silvopasture, policy makers need information on the economy-wide impacts that these policy changes would have on Florida. Our study aimed to fill that gap by investigating how policies requiring Floridian cattle ranchers to adopt silvopasture would impact the Florida economy. We also aimed to analyze how the well-being of households in Florida will be impacted by these policies. Study Objectives Our primary objectives were to analyze economy-wide impacts of a policy requiring all cattle ranchers in Florida to adopt silvopasture practices. Policies that required ranchers to adopt silvopasture would result in a decrease in the land available for cattle production by ranchers. They would simultaneously cause the operating costs of ranchers to increase. This study simulates the effects of both of these shocks being enforced on the cattle-ranching sector. This task was addressed through a computable general equilibrium analysis of the economy of Florida. The next two research questions were addressed. Question 1: How will the modeled changes impact the overall economy of Florida?

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3 Question 2: How will the welfare of Floridians change as a result of the simulated shocks when the environmental benefits of the policies are taken into account? These two questions were explored under wage-flexible and wage-rigid scenarios.

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CHAPTER 2 RANCHING, RANCHING IMPACTS, AND SILVOPASTURE Cattle-ranching and the Cattle Industry Cattle-ranching in the North America began several hundred years ago, at the time of colonization of the New World. It provided an innovative land-use strategy that facilitated the settlement of the frontier, many times at the expense of native peoples (Jordan 1993). Since the time of colonization, cattle-ranching has managed to successfully spread throughout the United States. Over the last few decades the area of land being used for livestock production has been growing. In 1984, nearly 1 billion of the approximately 2.2 billion acres in the United States were grazed by livestock (Taylor 1984). Today, rangelands and pastures are found in all 50 states, and some studies estimate that they account for 55% of the countrys land surface area (Weltz et al. 2003). The cattle or beef industry is a major component of the agriculture industry in the United States. In 2003, the retail equivalent value of the United States cattle and beef industry was estimated at $70 billion dollars and the value of calf and cattle production was estimated at $33.2 billion (USDA 2003). The large difference between the two estimated values is due to the great deal of segmentation in the U.S. beef industry. The beef industry includes various loosely interlocking segments such as seed-stock producers, commercial cow calf producers, stocker operators, feeders, packers and retailers (Taylor 1984). However, our study was primarily concerned with the portion of the cattle industry that includes the raising of cattle on rangelands and pasturelands. 4

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5 The United States as a whole is a net importer, and is the largest importer of beef from the world market. Before the disruption of the beef trade due to discovery of BSE 4 in the U.S., Canada was the largest source of imported beef for the United States, and Japan was the largest purchaser of U.S. beef exports. With nearly 100 million cattle and calves, the U.S. has the forth-highest cattle population behind India, Brazil and China. (FAS 2004). The Florida cattle-ranching industry, which contributes more than $300 million to the Florida economy annually, is a major agricultural enterprise and has a significant influence on the states economy. According to USDA census data for Florida for 2002 (USDA 2002), there are approximately 1.74 million cattle in the state on over 19,000 ranches, making Florida the tenth largest cattle producing state in the U.S. Due to the large size and nature of this industry, it can have significant impacts on environmental quality. Environmental Impacts of Ranching The scale of the cattle industry in the United States makes it difficult for it to operate without impacting the environment to some extent. There are two main ways that the cattle industry adversely affects the environment. First, water pollution problems can result when water in the form of rainfall runoff comes into contact with manure and carries high concentrations of solids, nutrients, and disease organisms into surface waters and ground waters. Polluted runoff is the major contaminant of U.S. waterways according to the Environmental Protection Agency (EPA 1972). Water quality surveys conducted in twenty-two states found that out of 694,000 miles of river, 35,000 miles were adversely affected by animal feeding operations. Nitrogen and Phosphorus are both nutrients often 4 BSE or Bovine Spongiform Encephalopathy (more commonly known as mad cow disease) is a fatal neurodegenerative disease in cattle which may be transmittable to humans (FDA 2004).

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6 associated with accelerated eutrophication.of surface water. Algae blooms of Pfiesteria piscidida and Cytosporidium in drinking water may be associated runoff from animal waste (Baker 1999). Phosphorus management strategies are often identified as important in limiting surface water eutrophication from agricultural sources since blue-green algae are able to utilize atmospheric nitrogen, thus leaving phosphorus as the limiting developmental factor for blue-green algae. (Gerber et al. 2004) Environmental degradation from cattle-ranching is not, however, limited to water pollution. Global climatic change in the form of global warming can be attributed to several sources. The production of carbon dioxide through burning of fossil fuels and other sources, the production of methane, the release of nitrous oxide primarily from the application of fertilizer, and the production of ozone are major sources of the greenhouse gasses that influence global warming (Milich 1999) Cattle-ranching contributes to global warming through the greenhouse effect via the production of the greenhouse gas, methane. Methane is the second most significant greenhouse gas and is expected to contribute to 18% of the global warming from now until the year 2050. The largest source of methane emissions, 30%, is enteric fermentation from livestock, followed closely by methane emissions from rice paddies at 25 %. Also, due to the combination of factors such as their great numbers, large size, and high energy intake; cattle produce 70% of global methane produced by animals, humans included (Milich 1999). The quantity of methane released to the atmosphere is much less than the quantity of carbon dioxide, however, methane is twenty times as effective in trapping heat on a per molecule basis (Harrington and Lu 2002).

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7 Regardless of the environmental impacts associated with cattle production, the worldwide consumption of beef is not likely to decrease dramatically in the foreseeable future. It is, therefore, necessary for society to seek solutions to help mitigate the environmental impacts of ranching while allowing producers to continue to provide the goods that people desire. The adoption of silvopasture practices by ranchers has been suggested as a possible means of helping to mitigate these environmental impacts. Benefits Associated with Silvopasture Silvopasture is a form of agroforestry that combines spatial and rotational growth of timber, forage, and livestock, has many associated environmental benefits (Husak and Grado 2000). Silvopasture may be able to mitigate of some of the negative impacts of cattle production while, in addition, providing other environmental services to the public. There are many benefits associated with silvopasture, which fall into several categories such as water quality improvement, soil conservation, carbon sequestration 5 and improvement of wildlife habitat (Shrestha and Alavalapati 2004). In a recent study, Shrestha and Alavalapati (2004) estimated the publics willingness to pay for these environmental services. Their research suggests that households in the Lake Okeechobee Watershed in Florida would be willing to pay $30.24 $71.17 per year for five years to receive these environmental benefits. Benefits Mitigating the Impacts of Cattle-ranching The adoption of silvopasture practices by ranchers would help to mitigate the negative impact that cattle-ranching has on water quality. Growing trees on farms and ranchlands would improve the quality of water through the reduction of pollution runoff, 5 Carbon sequestration is the removal of carbon dioxide from the atmosphere and its storage in the form of biomass in the terrestrial biosphere. (Albrecht and Kandji 2002)

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8 the replenishment of ground water aquifers, and the maintenance of the long-term water cycle. (Wu et al. 2001, Stednick 1996) Many silvopasture arrangements include tree and grass buffer strips as part of their overall design. Research suggests that tree and grass buffer strips twenty to thirty meters in width control up to 77% of phosphorus and 80% of nitrogen runoff (EPA 1995; Gerrett et al. 2000). Reduction in the quantities and stocking rates of cattle supported by silvopasture cattle ranches as opposed to conventional ranches would also have the effect of mitigating pollution by the reduction of the quantity and the concentration of animal wastes as the number and density of animals is reduced. Adoption of silvopasture would also help to mitigate the negative effects that cattle-ranching has on the atmosphere through carbon sequestration (Shrestha and Alavalapati 2004). Carbon sequestration has been shown to be a cost effective means for mitigating global climatic change by compensating 6 for greenhouse gas emissions (Albrecht and Kandji 2002, Zhang and Xu 2003). The quantities of carbon dioxide stored as a result of adding tree cover can be substantial. According to recent literature, an acre of southern pine grown in silvopasture on a twenty year rotation could absorb anywhere between 145 to 220 tons of carbon dioxide (Cannell 1999, Grierson et al. 1992). As with the reduction in water pollution, reduction in the quantities and stocking rates of cattle supported because of adoption of silvopasture on cattle ranches would also cause a reduction in greenhouse gas emissions locally. This portion of the mitigating effect of silvopasture adoption might be reduced to some extent, however, if imports to the region increase signifying increased production in foreign regions. 6 The sequestration of carbon would not directly reduce the amount of methane in the atmosphere, however, since both are greenhouse gasses, reduction of atmospheric carbon can help to offset methane emissions in the global warming context.

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9 Additional Benefits Throughout Florida, private pasture and ranchlands play an integral role in providing habitats for a diverse selection of wildlife species. Some of the species inhabiting these areas include the white-tailed deer, the Sandhill crane, and the Burrowing owl (Morrison and Humphrey 2001, Swisher et al. 2000). Many of the species that the trees and vegetation on these lands provide habitats for are threatened or endangered. Additionally, studies have been conducted in other states suggesting that agricultural lands that include wildlife habitat command higher prices per acre than similar land dominated by agricultural production (Bastian et al. 2002). This increased value may be attributed to the increased opportunities for hunting and wildlife watching on the land. Silvopasture may also be more aesthetically pleasing than open pastures while the additional tree cover could provide livestock with increased protection from summer heat and winter chill (Nowak et al. 2002) Possible Externality Dilemma The above reasons provide explanation for the increase in public interest in the incorporation of silvopasture technology on ranchlands as a means for realizing environmental services that they prefer to have. However, the adoption of silvopasture may result in a positive externality 7 problem for ranchers. In general, the environmental services or benefits that would be provided through silvopasture technologies on ranchlands are external to the production decisions of ranchers. Ranchers would incur increased management costs as well as reduced cattle output as a result of adopting 7 Positive externalities exist when the marginal social benefit of production and or consumption exceeds the marginal private benefit i.e. production and/or consumption generate external benefits that may go under-valued by the market (Jones 2004).

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10 silvopasture on their lands (Shrestha and Alavalapati 2004). Some examples of these increased costs include: establishment costs associated with tree planting, purchase of fencing, and temporary withdrawal of livestock from the areas. While cattle ranchers costs would be increasing, the benefits of their actions would be enjoyed by the public. There is no direct market mechanism for the public to pay the ranchers in compensation for these increased costs. However, cattle ranchers may be able to pass on some or all of these increased costs to consumers through raising their prices and not be worse off. This provides some rationale for the government to seek information regarding the economic impacts of policies requiring ranchers in Florida to adopt silvopasture practices. If research shows that the cattle-ranching industry is severely negatively impacted by being required to adopt silvopasture practices, then policies that would serve to internalize the benefits that ranchers would provide to the public through adoption of silvopasture might be justified. Such policies might include additional taxes on households that the government could transfer to the cattle ranchers in exchange for provision of the environmental services the public desires.

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CHAPTER 3 LITERATURE REVIEW Introduction In this chapter a review of review of general equilibrium modeling techniques is provided. Next, the structure of general equilibrium models is discussed, followed by a review of literature on applications of CGE models. Finally a review of some examples of how cost benefit analysis has been applied to economics and the environment is provided. Why Use Computable General Equilibrium Modeling? Policy makers require information concerning the probable effects of implementing policies that would require ranchers in Florida to adopt silvopasture practices since such policies could drastically influence Floridas economy. Analysts often utilize partial equilibrium analysis to determine the possible effects on an industry as a result of policy actions. While partial equilibrium analyses provide highly detailed information on the likely effects of policies to one particular industry, their downside is that they neglect intersectoral interactions within the economy. In order to address the economy-wide impacts of policies in a more comprehensive manner, general equilibrium modeling techniques have been developed and applied to policy analysis. General Equilibrium Modeling Introduction General Equilibrium modeling techniques include several modeling methods, each of which is employed by policy analysts and researchers to conduct economy-wide 11

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12 impact analyses. As the techniques progress from the relatively simple input-output models to the increasingly more complex social accounting and computable general equilibrium models, the data requirements for the methods increase greatly. Likewise, the capacity of the modeling techniques to capture economy-wide impacts increases as the models become more general. The availability of data, the time and funding available for the analysis and the specific characteristics of the situation to be modeled are considered in selecting an appropriate model. Input-Output Models Input-output (I-O) models are the simplest of the three types of regional modeling systems presented. The essence of I-O models is that industries are related to each other through transactions between them through the buying and selling of raw materials (Pyatt, 1999). Of the general equilibrium modeling techniques discussed, I-O models have the benefit of having the lowest data requirement. There are several characteristics of I-O models that can, however, limit their ability to adequately analyze regional impacts of policies (West, 1995; Miller and Blair 1985). The first of these characteristics is that input-output models have infinitely elastic supplies of inputs into production processes, resulting in a lack of supply-side constraints. Therefore, I-O models offer no market feedback mechanism between primary factors and final commodity demands (West 1995). The failure of this model type to show these connections is of concern because while sectors may not be linked directly by commodity flows in the economy, they still might interact through competition for scarce resources, most notably competition for primary factors of production (West 1995). Therefore, I-O models may only be appropriate for use in modeling situations where primary factors of

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13 production are less than fully employed and where producing sectors have excess capacity (Patriquin 2000). Another limiting characteristic of I-O models is that prices of inputs and outputs are fixed therefore preventing the model from being able to capture the behavioral adjustments of consumers and producers that would occur in the face of endogenous prices. This limitation implies that I-O models may be appropriate only for the extremely short-run (Patriquin 2000). Another assumption of I-O models that restricts their application to the extreme short-run is that in input-output models, production technologies are assumed to have fixed input proportions therefore preventing substitution between factors of production. Alternative modeling methods have been designed because of the inherent limitations of I-O models. Nevertheless, I-O models still serve a useful purpose as descriptive tools of economies. Additionally, they often serve as the base for other more complex modeling procedures (West 1995). Social Accounting Matrices The social accounting matrix (SAM) is the next more complex method presented for modeling regional economic policy impacts. Similar to I-O models, SAM models represent inter-industry linkages in the economy. In both I-O and SAM frameworks, the purchase of an intermediate input by one sector represents the sale of that same input by another sector. However, because the SAM utilizes double-entry bookkeeping, each transaction appears in the accounts of two different sectors, rather than in a single cell as represented in I-O (Robinson et al. 1999). Additionally, in the SAM model framework, income for each sector must be equal to that sectors total expenditures. Total expenditures for an industry could include costs

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14 such as intermediate inputs, wages, imports, as well as capital services. Incomes appear along the rows of social accounting matrices and expenditures down their columns. The budget constraint, therefore, requires that the row and column sums must be equal (Robinson et al. 1999). Similar to I-O models, SAM models can be utilized to model economic impacts. However, they have the added benefit of being able to capture distributional impacts as well. For example, households could be disaggregated within the SAM framework based on household income levels therefore giving them the ability to describe which segment of households would win or lose based upon a given policy shock (Stone 1985, Patriquin 2000). SAM models are more complex and provide a greater level of sophistication in their ability to capture more detailed aspects of economic changes than IO models. However, because SAM and IO models are based on similar assumptions, SAM models are still vulnerable to some of the inherent limitations of IO models. Neither SAM nor IO models account for supply constraints or the substitution between inputs (Adelman and Robinson 1986). Additionally, in SAM models, the technical coefficients remain fixed as do prices. Because these limitations can reduce the applicability of both IO and SAM models, considerable effort has been devoted to deriving, developing and applying computable general equilibrium models. Computable General Equilibrium Computable general equilibrium (CGE) models represent then next more complicated building block in modeling that will be presented. CGE models incorporate a set of behavioral equations that describe the economic behavior of the agents identified

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15 in the model as well as the technological and institutional constraints that they face (Thissen 1998). These sets of equations are responsible for the enhanced flexibility and applicability of CGE models. The incorporation of these behavioral equations makes CGE models more robust than their predecessors because these models are able to capture certain economic behavioral relationships and characteristics that the neither IO nor SAM was capable of integrating into its framework. One such advantage of CGE models is their ability to handle endogenous prices therefore permitting the prices of inputs to vary with respect to changes in output prices. This feature allows the responses of CGE models to economic shocks to more closely approximate the responses of agents found in the economy. Another advantage of CGE models is that they can include constraints on the availability of primary inputs. This feature of CGE models is significant because of the dampening effects that this type intersectoral linkage, based on resource limitations, can provide. Whereas in IO and SAM models, expansions in one industry lead directly to expansion in other industries based on the technical coefficients linking the industries in the models. These dampening effects can be seen when an increased quantity of a limited factor of production such labor, for example, is needed for an expanding industry. This industrys increased demand for labor will limit the supply of that factor available for other industries, resulting in a certain degree of contraction 8 in other industries competing for that factor (Patriquin 2000). 8 The actual degree of contraction for industries competing for the same primary factor as is an expanding industry will be dependent on several factors including, but not limited to: the production structures of those industries, factor intensities, and the technical coefficients linking models industries to each other.

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16 CGE models also have the distinct advantage of being highly customizable. For example, a CGE model could be constructed with the assumption that production structures allow no substitution between intermediate inputs and primary factors, similar to the structure of I-O models. In CGE models, assumptions such as these can also be relaxed or modified to a desired level depending on the specific characteristics of the economy being analyzed by the model (Alavalapati et al. 1998). Although CGE modeling techniques offer several advantages over both I-O and SAM, they are not without weaknesses and limitations. One weakness of CGE models is that many CGE models are deterministic models. In other words, for a given data set and specified shock, a CGE model will determine one set of outputs without allowing for uncertainty within its framework (Xie 1996). Also, Shoven and Whalley (1984) pointed out that no consensus exists regarding the determination of the values of elasticities and other key parameters, yet they often play a pivotal role in the specification of CGE models. Another disadvantage of CGE models is that they frequently require large amounts of data. Often, to reduce the demand for data for CGE models, the models are calibrated on a benchmark data set for a single year. While making the models quicker and easier to construct, this can result in the models being very sensitive to one year data (Xie 1996). In addition, earlier CGE models were either static or only quasi-dynamic in nature and treat inter-temporal behavior such as investment inadequately. CGE models have also been criticized for being too complicated to understand for decision makers and the general public. As research has moved forward on the construction and application of CGE models, many of their limitations and weaknesses have been addressed. Investment

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17 behavior has received more rigorous treatment in recent CGE models (Xie 1996). Cattaneo (2001), for example, combines factors such as agents marginal propensity to save, investment demands, and planning horizons to develop a robust dynamic macroeconomic model of the effects of deforestation in the Brazilian Amazon. Also, econometric approaches have been more widely adopted for the estimation of parameters utilized in their behavioral equations (Patriquin 2000). In the following sections, a brief description of CGE modeling history and application is provided. Computable General Equilibrium Modeling Computable General Equilibrium Models for Policy Analysis Over the past several years, CGE modeling has become an increasingly popular tool among researchers for policy analysis. They have been used to address a multitude of policy issues including: choice of development strategies, trade policies, income distribution, long term growth and structural changes, and structural adjustments to external shocks (Bandara 1991). In addition, much effort has been spent recently to develop CGE models that can simultaneously capture the above economic policy analyses while also describing the effects of the modeled policy shocks on the status of the environment. Much of basis for current CGE modeling techniques originated several decades ago with the work of a small group of economists. One of the most notable is Lief Johansen, whose multi-sector growth model was the first empirically based price endogenous model analyzing resource allocation issues Although the model was originally developed as a forecasting tool, Johansen applied the model to answer policy questions in Norway (Shoven and Whalley 1984). Another important figure in the inception of CGE development was Arnold Herberger. In his 1962 article, The Incidence of the

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18 Corporation Income Tax Herberger was the first economist to investigate taxes numerically in a two-sector general equilibrium framework (Shoven and Whalley 1984). Equally important was Ronald W. Jones paper, The Structure of Simple General Equilibrium Models. In this paper, he provides a detailed analysis of the structure of the simple competitive model of production, highlighting the similarities existing among several problems in comparative statics and economic growth (Jones 1965). This paper proved to be influential in the developmental course of CGE modeling. As the number of CGE models began to increase, so too did the range of policy issues addressed by modelers. CGE models designed to analyze various impacts of changes in trade policies emerged. These trade policy impact analysis models frequently belonged to one of two major categories. The first major category was comprised of single-country models designed to investigate how developments abroad affected individual economies. The second major category contained multi-country models, which were designed to tackle global trade issues. (Shoven and Whalley 1984) Both of these two different types of trade models often incorporated the Armington 9 assumption, which differentiates home and foreign goods as imperfect substitutes in consumption. (Blonigen and Wilson 1999) Examples of CGE models utilized to analyze trade policies include: Dervis et al. (1982), Dixon et al. (1985), Mercenier and Waelbroeck (1985), and Shoven and Whalley (1984). Models have also become more complex in their ability to capture inter-temporal features within their structures. Through the inclusion of equations describing how an 9 Armington Assumption The assumption that internationally traded products are differentiated by country of origin. This assumption is now standard in international CGE models, and is used to generate smaller and more realistic responses of trade to price changes than implied by homogeneous products.( Deardorff, 2001)

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19 economy evolves, inter-temporal CGE models can be used to describe the manner in which an economy reaches its equilibrium. This is a desirable feature to incorporate in CGE models because policy makers are often concerned with the rate at which an economy will move towards the long run equilibrium point as well as other transitional characteristics (Dixon et al 1999). However, inter-temporal models have the downside of being more complex to construct and harder to solve, and thus are more costly. Nevertheless, many dynamic models have now been constructed. These models can increase the meaningfulness of welfare change calculations in comparison to static modeling because of the abundance of dynamic elements in the real world (Seung and Kraybill 1999). Examples of inter-temporal, or dynamic, CGE models can be found in: Keuschnigg and Kohler (1994), Wang (1999), and Deepak et al. (2001). Computable General Equilibrium and the Environment Policy makers decisions regarding environmental legislation implementation is often reliant on whether or not the legislation will harm economic growth. Various interest groups constantly influence policy makers to obtain the outcome in their favor. On one side are industry representatives, who typically forecast increased unemployment, reduction in international competitiveness, and depression of economic growth. Environmental lobbyists, conversely, stress the negative consequences of factors such as pollution while they downplay trade-offs between economic growth and a clean environment (Wajsman 1994). In effort to supply policy makers with unbiased information, scientists are required to carefully examine issues and describe both the probable positive and negative consequences of proposed environmental legislation. A wide range of environmental-economic issues have been examined by CGE analyses. These environmental CGE models, although varying greatly in regional size

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20 and functional specification, typically fit into three general categories. The first category is comprised simply of standard CGE models that have been applied to address environmental issues. For example, in Olatubi and Hughes (2002), they use a general equilibrium model of Louisiana to analyze the effects of the Wetland Reserve Program 10 on the states economy. The second category includes environmentally extended CGE models. In order to provide more detailed descriptions of the environmental impacts of economic policy shocks, these models usually give indications of changes in pollution emissions using fixed coefficients per unit of sectoral output. In this type of model, these indicator outputs do not feed back into the behavioral equations of the CGE model, and therefore do not change the behavioral specifications of the models (Xie and Saltzman 2000). Models such as Patriquins (2000) environmentally extended CGE model of the Foothills Model Forest in Alberta, Canada belong to this category. Models that introduce environmental feedback to the economic systems belong to the last major category of environmental CGE models. Jorgenson and Wilcoxen (1990) specify pollution control costs in their production functions in order to achieve this environmental and economic integration. Alavalapati and Adamowicz (1999) utilize a simple general equilibrium model to study the interactions among tourism, other economic sectors, and the environment. In their tourism impact model, they specify the damage to the environment as a function of output and the extent of land used for production (Alavalapati and Adamowicz, 1999). 10 Wetland Reserve Program (WRP) The Food, Agricultural, Conservation, and Trade Act of 1990 (FACTA) amended the Food Security Act of 1985 (FSA) to provide for the establishment of the WRP. The goals of the program are to ensure no net loss of remaining wetlands and to increase the quality and the quantity of the nations wetlands. (Olutabi and Hughes, 2002)

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21 Cost-Benefit Analysis The interaction of the economy and the environment are not modeled endogenously in this research. However, we utilize willingness to pay results for the environmental services of silvopasture of the residents in the Lake Okeechobee watershed and compare these values to changes in the income of Floridian households in a cost-benefit analysis framework. Cost benefit analysis is a decision making tool used frequently by economists and other decision makers to determine whether or not a project is worth the necessary investment. This tool has been applied to many economic and environmental issues. Ervin and Dicks (1988) utilized a cost benefit analysis of the Conservation Reserve Program to analyze the economic welfare consequences of converting cropland to alternative uses to enhance conservation and environmental goals. Similarly, Moss et al. (1996) use a cost benefit approach to compare the social, financial, and ecological costs of several different land use options. Contribution The main contribution of this work will be estimating probable economy-wide impacts of policies requiring that all Floridian cattle ranchers adopt silvopasture. These impacts will be determined through the application of a five sector CGE model of the state. Additionally, this work will give some indication of possible changes in the welfare of Floridian households by comparing the perceived benefits of the services silvopasture provides with the costs to households.

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CHAPTER 4 MODELING METHOD Introduction This chapter outlines the construction of the model used in this research. First, information on the data source and aggregation is given. Next, the structure of the general equilibrium model used in the analysis is presented, including the equations modeled, the closures chosen, and the shocks implemented. Finally, a discussion of the cost benefit approach used to determine the change in the well being perceived by households in Florida under the two chosen closures is given. Florida Computable General Equilibrium Model Data The data utilized in the construction of the social accounting matrix for this model was obtained from the IMPLAN database of the Minnesota IMPLAN Group. The original 1999 database for Florida consists of 528 individual sectors or industries. Industries were aggregated into five sectors for the final SAM based on the goals of this research and the general industry product categories. The five aggregated sectors are cattle, other agriculture and resources, forestry, manufacturing, and services. Estimates indicate that the approximate the size of the cattle sector in Florida is approximately $300 million annually (Stainback et al. 2004). In aggregating sectors of the IMPLAN database, we determined that the size of the cattle sector in this model is approximately $188 million annually. This difference is most likely attributed to our selection of which industries to include in the cattle sector aggregation. For the model in this study, only range and ranch fed cattle are included in the cattle sector. Other value 22

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23 adding cattle related sectors, such as the various meat processing industries, are instead treated as part of the manufacturing sector. Model Structure The computable general equilibrium model that has been constructed in this study is a customized version of a Stylized Johansen Model. The development of the theoretical structure of a Johansen model includes formulating several sets of equations. Included in these are equations for: household and final commodity demands, intermediate and primary factor inputs, commodity pricing, and market clearing (Dixon et al. 1999). These equation sets form the framework for the model and determine how the model will react in response to shocks applied to the system of equations. Following the general structure from Dixon et al. (1999), a customized version of the Stylized Johansen Model is developed below utilizing the percentage change form equations necessary for solution of the computable general equilibrium model. iipyx0 for i = 1, . 5 (3-1) Equation 3-1 represents the household demand equation for the commodities in the model. It shows that the household demand (xi0) for a commodity will increase as the income increases, or the price of that commodity decreases. Each sector in the model produces one commodity, therefore resulting in five household demand equations, one for each commodity. 2161ffjfjtttjijijpppxx (3-2) for i = 1, . ,8, j = 1, . 5

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24 Equation 3-2 represents the input demands for each of the inputs for industries one through five. Equation 3-2 says that the change in the demands for inputs by a sector is a function of several variables. First, it shows that the demands for the inputs to a sector (xij) will increase as the demands for the sectors output commodity (xj) increase. It also shows that the change in the demand for input from a given source will be inversely related to the change in the relative price of that input. For this research, the factors of production were disaggregated from labor and capital only, to labor capital and land. In the Stylized Johansen model, both labor and capital are treated the same, with one model price each for both wages and capital rental rates, thus allowing both labor and capital to be mobile between the various producing sectors. In this research, however, we chose to model capital (and the disaggregated land factor) as sector specific, while allowing labor to remain mobile. This is accomplished by utilizing sector specific prices (pfj) in equation 3-2 for the capital and land factors of production inputs for the sectors. 2161ffjfjtttjjppp for j = 1, . 5 (3-3) Equation 3-3 is the percentage change form of the zero pure profits condition for the model. This equation shows that the percentage change in the price of good j will be a weighted sum of the percentage changes in the input prices for that industrys intermediate inputs and primary factors. The weights, or alphas, are the proportions of the cost that each input comprises in the total cost of all inputs for that commodity. The RHS of equation 3-3 is identical to the bracketed portion [*] of Equation 3-2.

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25 60jijijixx for i = 1, . 5 (3-4) Equation 3-4, the market clearing equation for the commodities of the CGE model, equates a weighted average of the percentage changes in the various demands for each commodity to the percentage change supply of that commodity. The weights, or betas, are the proportions of the demand that each source of demand (j) comprises in the total demand for each i good. For the purposes of this research, an additional demand, net export demand, was added to the model. Therefore, the sources of demand for a commodity in the model are: household demand (j = 0), intermediate demands from the five industries (j = 1,,5), and net export demand for the commodity ( j = 6 ). The addition of export demand causes the model to depart from the Stylized Johansen model by transforming the CGE model from a closed economy model to an open economy model. fjjfjfxx51 for f = 1,2, and 3 (3-5) Equation 3-5, the market clearing equation for the primary factors in the CGE model, equates a weighted average of the percentage changes in the demands for each factor to the percentage change in the quantity supplied of that factor. The factors in the model are labor (1), capital (2), and land (3). 02p (3-6) Equation 3-6 represents the selection of sector two as the numeraire, or the unit of measure for money in the model. The other prices in the model are given in terms of the price of the numeraire commodity.

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26 Additional equations For the purposes of this research, additional equations we added to the model in order to incorporate features not captured in a Stylized Johansen model. A new export demand equation was added in order to change the model from a closed economy to an open economy. In addition, an equation for labor employment was added, to allow the model to capture changes in the labor employment levels. iwiieppx for i = 1, . 5 (3-7) Equation 3-7 represents the percentage change form of the net export equation for the CGE model. It shows that export demand for a good (xie) is inversely related to the change in the price of that good (pi) resulting in a negative export elasticity of demand. More specifically, we have assumed, for simplicity of model construction, that the export elasticity of demand is negative one. Additionally, we assume that the economy of Florida is small relative to the rest of the world. Hence, we have chosen the world prices for the commodities (piw) as exogenous in the model. euleu (3-8) Equation 3-8 relates a weighted sum of the change in unemployed labor force and employed labor force to changes in the total employable labor force available. The addition of this equation gives the model the capability of capturing the effects of the policy shocks on the level of unemployment in the economy when the change in the employed labor force is chosen as endogenous.

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27 Table 4-1. Specification of the five-sector Florida CGE model 3-1 iipyx0 i = 1,,5 3-2 2161ffjfjtttjijijpppxx i = 1,,8 j = 1,,5 3-3 2161ffjfjtttjjppp j = 1,,5 3-4 60jijijixx i = 1,.,5 3-5 fjjfjfxx51 f = 1,2,3 3-6 02p 3-7 iwiieppx i = 1,,5 3-8 euleu The set of equations in the CGE model form a matrix. For this matrix to have a solution, the number of equations in the model must be equal to the number of endogenous variables. By nature of their construction, these models will have more variables than the number of equations. As a result, some of the variables must be selected as exogenous, and the rest retained endogenous in order to fulfill the mathematical requirements for finding a solution to the model. Below is a list of the variables that we have chosen to retain endogenous in the model.

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28 Table 4-2. Endogenous variables y Household income pi i=1,,5 Commodity price pfj f=2; j=1,,5 Price of land xi i=1, Demand for commodities xf f=2 Demand for capital xf f=3 Demand for land xi0 i=1,...,5 Household consumption of commodities xij i=1,,5; j=1,,5 Intermediate commodity inputs xfj f=1; j=1,,5 Labor factor input demand xfj f=2,3; j=1,,5 Capital and land factor input demands xie i=1,,5 Net export demand for commodities u Unemployed labor force pt t = 6 Price of labor ( wage rate ) xf f=1 Total demand for labor The rest of the variables, which are listed in Table 4-3 are selected as exogenous. Two of these variables, the aggregate supply of labor, and the wage rate, are utilized to create the two different closures that we utilize in the analyses in this research. Only one of the two will be exogenous at a given time. For the flexible wage scenario, the aggregate supply of labor will be selected as exogenous and the wage rate will be retained endogenous. For the rigid wage scenario, the wage rate will be selected as

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29 exogenous and the aggregate supply of labor will become endogenous. This will allow changes in unemployment to occur in the second model closure. Table 4-3. Exogenous variables piw World price of commodity l Employable labor force in the economy pfj f=2; j=1,,5 Price of capital xfj f=3; j=1,,5 Supply of land to industries xf f=1 Total demand for labor pt t = 6 Price of labor (Wage rate) Model closures The way that equilibrium is ensured in a CGE model is known as the closure of the model and is determined by selecting the set of variables that will be exogenous to the system. The chosen closure has significant theoretical implications directly affecting the behavioral characteristics of the model. Each of the two main schools of closure has its merits. Acknowledging that there are significant differences in the ways the model will react depending on the closure selected, we have chosen to model the shocks under two different main closure scenarios. This treatment should provide policy makers with more information on how the proposed policy shocks could influence the economy. The first closure we have chosen to enforce on the model has a variable wage rate, with the aggregate labor demand fixed as exogenous. This closure forces wage rate to vary such that full employment is ensured. This selection is similar to the Johansen 11 closure and 11 In the Neoclassical closure, aggregate investment is determined by aggregate savings, which in turn are determined endogenously through the fixed savings rate out of after tax income and the government deficit. In Johansen closure, aggregate investment is assumed to be fixed exogenously and the savings rate is

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30 falls within the main school of Neoclassical closures. In the second closure, which is more structuralist in nature, labor employment is endogenous and the fixed the wage rate and forces the level of employment to vary to achieve equilibrium in the model. Table 4-4, derived from the model closure discussion in Kraev (2003), highlights some of the differences between these two main schools of closure for CGE modeling. Table 4-4. Closures Structuralist Neoclassical Elasticity Micro Macro Full employment Yes Yes Not necessarily (can restrict prices and labor mobility) Not necessarily (emphasize macroeconomic disequilibria) Full capacity utilization Yes Yes Not necessarily (neoclassical disequilibrium) Not necessarily (disequilibrium possible) Marginal productivity determines prices Yes Yes Not necessarily Not necessarily Substitution elasticities Perfect substitutes Limited substitution (Armington assumption) Varies (limited or perfect substitution) Varies (limited or perfect substitution) Characteristics that separate some of the different closure types are given with a list of the main closure categories across the top. The only characteristic that separates a purely neoclassical closure from an elasticity-structuralist model is the utilization of limited substitution elasticities (Kraev 2003). Modeling Shocks to the Existing Equilibrium The construction of the computable general equilibrium model establishes a base, static, or equilibrium condition of the economy. This condition is described by the calibrated data in the SAM database that is used as an input for the CGE model. The equation sets explained above create the structure of the economy being modeled and assumed to generate the required savings. Johansen explicitly argues that macroeconomic fiscal and monetary policies, presumably outside the CGE framework will ensure that savings are generated to balance the investment.(Robinson 2003)

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31 describe how that economy will react in the event of a shock to the economic system. Once the model is in place, many simulations may be performed depending on the specific shock, or shocks, that the modeler has chosen to enforce on the model. Below, we describe the rationale for the shocks that we modeled. Shocks Modeled Ranchers would have to modify the composition of their ranchlands in the course of changing from traditional ranching operations to silvopasture. Planting additional trees on these lands will reduce the land area available to cattle ranchers for production of their livestock. In order to model the effects of the ranchers implementing this operational change, we chose as an exogenous variable the land factor of production for sector one, which represents the quantity of land available to the cattle industry for production. We then impose a twenty-five percent reduction in the cattle sectors available land base by applying a shock of -25% to the supply of the land factor of production for sector. Recently, research has been conducted on the values of trees or forests on ranchlands. That research was modeled such that for silvopasture adoption by ranchers, 20% of land would be taken out of production from ranching with additional lands taken out for the creation of riparian buffer strips (Shrestha and Alavalapati 2004). The level of environmental improvement offered by this size of land use change is similar to the level of improvement on which the willingness to pay data that was utilized in this study was also based. For that reason, a value of 25% was chosen for the negative shock to the land base for ranchers to include the change in land available due to adding trees to the ranchlands as well as to account for additional land for riparian buffers. In addition, the adoption of silvopasture will cause ranchers to expend more in capital costs on items such as tractor and other timber management equipment rental

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32 required to practice silvopasture. The actual increase in capital costs for ranchers adoption of silvopasture could vary greatly depending on factors such as the size of the ranching operations, the method chosen to protect young trees from cattle, and the amount of the necessary equipment already owned by the rancher. Because of the great deal of variation possible in cost increases, a twentyfive percent increase in capital costs was chosen in order to ensure this portion of the total shock would be significant in comparison to the shock to the land base. This is simulated in the model by applying a 25% increase to the cost of capital for the cattle sector. The effects of each shock are analyzed under each of the two closure scenarios wage-flexible, which ensures no change in employment, and wage-rigid, which allows for changes in employment. Cost-Benefit Analysis This analysis is conducted in order to give policy makers more information on the effects on the welfare of Floridians due to policies on silvopasture. In order to conduct a cost benefit analysis, both the costs and the benefits have to be expressed in the same units. The most common unit for both to be expressed in is in currency or dollar values. We utilized the change in the income of the households in Florida for each scenario as the cost side of the cost benefit analysis. The determination of the benefits side of the analysis is slightly more complicated, however. Because there is no direct market for the environmental services of silvopasture, alternative means of valuing these benefits must be utilized in order to compare their benefits to the costs in monetary terms. In order to address this problem, we utilize a study that was conducted to estimate the publics willingness to pay for these environmental services. Shrestha and Alavalapati (2004) determined that, within the Lake Okeechobee watershed, the publics willingness to pay for the environmental services associated with silvopasture totaled $924.4million.

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33 There are 1.34 million households in the Lake Okeechobee watershed and 6.34 million households in the entire state of Florida (Shrestha and Alavalapati 2004). However, without having more information relating to the intensity of preferences for these benefits statewide as compared to the intensity of preferences in the Lake Okeechobee watershed, an accurate extrapolation cannot be calculated. Therefore, we have chosen to utilize the WTP estimate for the Lake Okeechobee watershed as a conservative estimate of the total willingness to pay of all Florida households. Another complication in using this WTP value directly is that this willingness to pay estimate is the value that these households attach to having these benefits forever. The WTP value must therefore be converted to a yearly amount in order to compare it with the results from the CGE analysis, which are based on yearly data from 1999. ipmtPV/ The yearly contribution necessary to realize this benefit forever can be calculated by utilizing the above equation for a perpetual annuity. We chose a common discount rate of .05, or 5% to calculate the present value. We acknowledge that with higher estimates of the discount rate the estimate yearly benefit will increase. Additionally, if environmental services provided by silvopasture are not inferior goods, then the publics desire for the environmental services of silvopasture will decrease as household income decreases. Income elasticities of demand for environmental services similar to those provided by silvopasture have been estimated in Sweden. Hokby and Soderqvist (2001) estimated that the income elasticity of demand for reducing nitrogen loads in waters in Sweden were about 1.10. Also, the income elasticity of demand for preserving agricultural landscape in Sweden was estimated at 0.91 by

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34 Drake et al. (1991). We assume that the elasticitiy of demand for the environmental services provided by silvopasture are comparable and we will therefore use an income elasticity of demand of 1.0 to evaluate the change in demand for these environmental services as income changes.

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CHAPTER 5 MODEL RESULTS Simulation Results The customized Five Sector Florida CGE model described in chapter three is utilized to simulate the impacts of cattle ranchers in Florida adopting silvopasture practices on their ranch and pasturelands. The simulations were conducted under two closure scenarios. Under the first closure, the wage rate was endogenous to the model and flexible. Under the second closure scenario, however, the wage rate is held constant, or rigid, by making the wage rate exogenous and applying no change to the price of labor. Wage-flexible Scenario Two simultaneous shocks, a 25% decrease in the land base available for cattle production and a 25% increase in capital costs for the ranching sector, are imposed on the CGE model for each of the two closure scenarios. The 25% decrease in land base available for production of cattle represents land that will be taken out of cattle production and instead be utilized for growing trees. The increase in capital costs in sector one represents additional capital costs, such as tractor and other timber management equipment rental required to practice silvopasture. The shocks simulate the effects that adopting silvopasture will have on the cattle-ranching sector directly. The model then simulates, through the CGE framework, how the changes imposed on the cattle sector will affect the rest of the economy of Florida. The results of the wage-flexible scenario are presented in Tables 5-1, 5-2, and 5-3. 35

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36 Table 5-1. Macro-economic impacts of -25% land base and +25% capital costs Variable % Change Original level New level Change Total household expenditure(millions) -0.009218 $240,336.56 $240,314.41 -$22.15 Wage rate -0.006444 1.00 0.99993556 -0.00006 Percent unemployment 0 3.9 3.9 0 Table 5-1 presents some of the macroeconomic impacts of the shocks on the Florida economy. Household demand for goods has dropped, -$22.15million, reflecting the negative effect on the income of Floridians as a result of these environmentally benign policies. Although this is a large change relative to other magnitudes in this simulation, it reflects a drop of just under one one-hundredth of a percent of the total expenditures of Floridians. The wage rate drops only slightly, -0.0006%, to keep employment levels constant at the 1999 level for Florida of 3.9%.

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37 Table 5-2. Commodity market impacts of -25% land base and +25% capital costs Sector % Change Original level ($) New level ($) Change ($) Price of commodity 1 3.03419 1.00 1.03 0.030 2 0.00000 1.00 1.00 0.000 3 -0.00082 1.00 1.00 0.000 4 0.00060 1.00 1.00 0.000 5 -0.00436 1.00 1.00 0.000 Total commodity demand 1 -2.94759 188.56 183.00 -5.558 (levels in millions) 2 -0.00286 7,694.74 7,694.52 -0.220 3 0.00074 405.84 405.84 0.003 4 -0.00521 54,213.16 54,210.33 -2.825 5 -0.00232 416,915.56 416,905.89 -9.668 HH commodity demand 1 -2.9540 0.00 0.00 0.000 (levels in millions) 2 -0.0092 1,126.35 1,126.25 -0.104 3 -0.0084 1.90 1.90 0.000 4 -0.0098 17,089.29 17,087.62 -1.677 5 -0.0049 222,119.02 222,108.23 -10.782 Export demand 1 -2.94507 61.00 59.20 -1.796 (levels in millions) 2 0.00000 3,969.55 3,969.55 0.000 3 0.00081 400.36 400.36 0.003 4 -0.00059 20,293.49 20,293.37 -0.121 5 0.00436 78,871.04 78,874.48 3.443 Table 5-2 presents the economic impacts to the markets for commodity outputs of sectors one through five for a 25% decrease in land base and 25% increase in capital costs for cattle ranchers in Florida under the flexible wage rate scenario. Changes to commodity prices, total commodity demands, total household demands, and export demands are shown. In addition, the pre-shock levels, post-shock levels, and level deltas are given. The commodity output results show that the price of sector ones output (commodity one) has increased by 3.03%. This increase in the output price of the cattle sector can be attributed to the increases in their input costs that are passed along to

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38 consumers 12 through raising the price of their product. The price of commodity two remains unchanged since it has been fixed as the numeraire. The manufacturing sector, sector four, also experiences a small increase of 0.0006% in the price of its output. One reason for this increase is because sector four contains many of the cattle consuming industries, such as meat packing plants as well as sausage and other beef processing industries. The price of their cattle input goes up, so they must adjust their output price as well to maintain zero pure profits. The shocks to the cattle sector have caused the overall economy of Florida to contract. As a result of this contraction, consumer demand for most of the sector outputs has declined. This drop in demand has the largest impact, in terms of dollar value decrease, to the service sector, sector five, which experiences a drop of $9.67 million. The service sector is the largest sector in the model however, and this drop reflects a change of only -0.0023%. The shocks were applied to the cattle sector directly, thus this sector experienced the largest percentage drop of -2.95% in demand following their relatively high price increase. As a result of the contracting economy, Floridian households have less income to spend on consumption of goods. Hence the demand for all commodities by households has decreased accordingly. Although the model shows that largest decrease in household demand by percentage is in sector one, households do not actually directly consume output from the cattle sector. Households instead purchase the processed cattle output from the manufacturing sector. This output carries along with it a higher price due to the 12 Consumers, in this case, refers not only to households in the model, but to all who purchase the output of sector one, including: sector one through purchase of its own output as an intermediate input, other sectors through purchase of intermediate inputs from sector one, and foreign importers purchase of commodity one.

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39 increase in intermediate costs of the input from sector one. This price increase along with the decrease in household expenditure causes the manufacturing sector to experience the second largest drop in consumer demand of the five industries, a decrease of -.0098%. Export demand changes are the least complicated changes to analyze with this model since we assume a constant exchange rate and the changes in export demand are therefore functions of only the change world price of the commodity and the change in the price of that sectors commodity. Because our treatment of Florida follows the small country assumption, changes in the production of goods in the Florida economy have no effect on world prices. We have therefore fixed world prices exogenously and export demand changes remain functions only of changes in the goods prices. Accordingly, there was a rise in net exports for commodities three and five and a decline in net exports for commodities one and four. The demand for net exports for commodity two remains fixed because of the selection of sector two as the numeraire in the model. Table 5-3. Factor market impacts of -25% land base and +25% capital costs Variable Sector % Change Original level ($) New level ($) Change ($) Labor Demand 1 0.00386 75.11100 75.11 0.00290 (levels in millions) 2 0.00358 3541.36792 3,541.49 0.12675 3 0.00636 15.22300 15.22 0.00097 4 0.00183 21113.47800 21,113.86 0.38574 5 -0.00024 215591.42200 215,590.90 -0.51742 Capital Demand 1 -20.03132 10.63860 8.50755 -2.13105 (levels in millions) 2 -0.00287 1102.93994 1102.90834 -0.03160 3 -0.00008 125.98700 125.98690 -0.00010 4 -0.00462 8250.00000 8249.61910 -0.38090 5 -0.00668 93277.50000 93271.26626 -6.23374 Land Prices 1 33.32833 1.00000 1.33328 0.33328 2 -0.00287 1.00000 0.99997 -0.00003 3 -0.00008 1.00000 1.00000 0.00000 4 -0.00462 1.00000 0.99995 -0.00005 5 -0.00668 1.00000 0.99993 -0.00007

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40 Table 5-3 presents the impacts to Floridas factor markets as a result of the shocks simulating the adoption of silvopasture by Floridas ranching sector. Since this scenario is under flexible wage rate assumption, aggregate demand for labor is fixed exogenously and the price of labor varies to maintain full employment of labor in Florida. Although the aggregate supply of labor is fixed in this closure, labor is not sector specific. This allows unrestricted mobility of labor within the economy. Each sector has its own degree of labor intensity. Thus, as demand for output from each of the sectors changes each sector will shift its demand for labor by the amount necessary, relative to its labor intensity, to maintain the desired level of output. This can be observed as the individual sectors adjust their employment levels as a result of the shocks. Sector five, which has a relatively large decrease demand for output, $9.67million, experiences in a decrease in its demand for labor even with the decrease in the wage rate. Labor from this sector then mobilizes and relocates to the other sectors, keeping the aggregate labor supply constant. Capital is sector specific in this model and therefore cannot move between sectors. The decrease in the output demand for sector one combined with the higher costs of capital in that sector, have resulted in a large drop in capital demand in sector one. This decrease in demand by sector one does not benefit the other sectors because of the immobility of capital. Therefore, the other sectors do not experience a gain in resources available that might be felt under a mobile capital model specification. The other four sectors each experience a slight reduction in capital utilization as a result of the contracting economy. Supply of land for all sectors was held exogenous in the model, but the land rental rates were allowed to vary. Land, like capital, is treated as sector specific, and sectors one (cattle-ranching), two (other agriculture) and three (forestry) are

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41 the land utilizing sectors of this model. As a result of the reduction to the land available for production of cattle for sector one, the rental rates for ranchlands have increased dramatically, 33.24%. The remaining four sectors each experience a slight decrease in rental rates. Wage-rigid Scenario Following the same reasoning for simulating the changes to the economy as a result of Floridas cattle ranchers adopting silvopasture as in the flexible wage scenario, identical shocks, consisting of a 25% decrease in the land base available for cattle production and a 25% increase in capital costs for the cattle-ranching sector, are imposed on the CGE model for the wage-rigid scenario. The results of the wage-rigid scenario are presented in Tables 5-4, 5-5, and 5-6. Table 5-4. Macro-economic impacts of -25% land base and +25% capital costs Variable % Change Original level New level Change Total household expenditure(millions) -0.137373 $240,336.56 $240,006.40 -$330.158 Wage rate 0 1.00 1.00 0.000 Percent unemployment 1.842082 3.9 3.9718412 0.072 Some of the key macroeconomic impacts of the shocks on the Florida economy are presented in Table 5-4. This closure represents a more short-term reaction of the economy to the imposed shocks. This is because, in the event of a shock to an economy, the initial response to changes in demand for labor will be met by changes in the unemployment rate rather than by changes in the real wage. (Domingues and Haddad 2003) The unemployment rate in Florida increases by from 3.90% to 3.97% as a result of imposing the shocks on the economy and the wage rate remained fixed exogenously by the closure. According to the U.S Census Bureau, there were 7,407,458 people employed in Florida in 1999. According to these employment levels, 5322 Floridians will lose their jobs as a result of the policy shocks when the unemployment level in Florida rise from

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42 3.90% to 3.97. As a result of this increase in unemployment and the contraction experienced throughout the Florida economy, households have much less income to spend and therefore total household expenditures decreased by over $330million. It is clear from these macro-economic responses that under this closure, the imposed shocks will have a more negative effect on the economy. Table 5-5. Commodity market impacts of -25% land base and +25% capital costs Variable Sector % Change Original level ($) New level ($) Change ($) Price of commodity 1 3.03727 1.00 1.03 0.030 2 0.00000 1.00 1.00 0.000 3 -0.00016 1.00 1.00 0.000 4 0.00510 1.00 1.00 0.000 5 0.00012 1.00 1.00 0.000 Total commodity demand 1 -2.98542 188.56 182.93 -5.629 (levels in millions) 2 -0.04269 7,694.74 7,691.45 -3.285 3 -0.00103 405.84 405.83 -0.004 4 -0.07390 54,213.16 54,173.10 -40.061 5 -0.09972 416,915.56 416,499.83 -415.732 HH commodity demand 1 -3.0813 0.00 0.00 0.000 (levels in millions) 2 -0.1374 1,126.35 1,124.80 -1.547 3 -0.1372 1.90 1.89 -0.003 4 -0.1425 17,089.29 17,064.95 -24.346 5 -0.1375 222,119.02 221,813.62 -305.400 Export demand 1 -2.94797 61.00 59.20 -1.798 (levels in millions) 2 0.00000 3,969.55 3,969.55 0.000 3 0.00016 400.36 400.36 0.001 4 -0.00510 20,293.49 20,292.46 -1.034 5 -0.00012 78,871.04 78,870.94 -0.095 The economic impacts to the markets for commodity outputs of sectors one through five for a 25% decrease in land base and 25% increase in capital costs for cattle ranchers in Florida under the wage-rigid scenario are presented in Table 4-5. The price change in the cattle sector under this closure was approximately the same as the change in the previous, wage-flexible, closure. This is due to the changes imposed on the cattle sector

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43 being much larger than other general equilibrium effects. In general the other commodity price changes are slightly higher in magnitude as apposed to the first closure, with the exception of the numeraire, whose price remains fixed. The demand for output from the cattle sector remains nearly the same as in the first closure because the price change for sector one is relatively large and therefore enforces a more binding constraint on the amount of commodity one purchased by the other sectors than changes in their input demands would impose. The remaining four sectors experience a large decrease in total demand. The largest portion of this decrease in total demand comes from the decrease in demand by households for those commodities. Household demand drops sharply because the impact on employment under the rigid wage scenario causes a large decrease in the amount of money households have to spend. As in the first closure, change in the net export demand is dependent only on the change in the commodity price.

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44 Table 5-6. Factor market impacts of -25% land base and +25% capital costs Variable Sector % Change Original level ($) New level ($) Change ($) Labor Demand 1 -0.03858 75.11100 75.08 -0.02898 (levels in millions) 2 -0.04269 3541.36792 3,539.86 -1.51174 3 -0.00119 15.22300 15.22 -0.00018 4 -0.06880 21113.47800 21,098.95 -14.52628 5 -0.09960 215591.42200 215,376.70 -214.71828 Capital Demand 1 -20.06023 10.63860 8.50447 -2.13413 (levels in millions) 2 -0.04269 1102.93994 1102.46912 -0.47082 3 -0.00119 125.98700 125.98550 -0.00150 4 -0.06880 8250.00000 8244.32392 -5.67608 5 -0.09960 93277.50000 93184.60027 -92.89973 Land Prices 1 33.19060 1.00000 1.33191 0.33191 2 -0.04269 1.00000 0.99957 -0.00043 3 -0.00119 1.00000 0.99999 -0.00001 4 -0.06880 1.00000 0.99931 -0.00069 5 -0.09960 1.00000 0.99900 -0.00100 The impacts under the wage-rigid scenario to Floridas factor markets as a result of the shocks simulating the adoption of silvopasture by Floridas ranching sector are presented in Table 4-6. Since wages are fixed exogenously under this closure, industries are forced to reduce their employment levels in order to reduce total spending on wages to meet the new, lower desired levels of output while minimizing costs. The largest impacts are in the service and manufacturing sectors, which reduce spending on labor by $214.7 million and $14.5 million respectively. As in the previous closure, capital is sector specific. Therefore capital cannot move between the models five sectors. In sector one, the combined effects of the decrease in the output demand for sector one and the higher costs of capital in that sector have resulted in a large drop in capital demand in sector one. As a result of the contracting economy, the service sectors capital expenditures decreased by $92.8 million. The

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45 manufacturing sector also experienced a large reduction in capital expenditures of $5.7million. Due to the reduction to the land available for production of cattle for sector one, the rental rates for ranchlands have increased dramatically, 33.19%. Because land is sector specific as well, an increase in the price of ranchland has no direct effect on the prices of other land in the model. Therefore, the decrease in output levels and therefore the desire for land input causes the land rental rates to drop since land quantities are exogenous. The model produced a large number of outputs for changes in the intermediate demands by sectors. These changes in the demands for each sectors intermediate good input are not presented here. However, the full set of output data for each closure scenario is available in Appendix A. Cost-Benefit Analysis The introduction of these shocks simulated the effect on the economy of cattle ranchers across the state of Florida adopting silvopasture. Impacts to the economy of Florida varied greatly, depending on the closure chosen, with a decrease of household income of $22.16 million for the wage-flexible closure and $330.16 million under the wage-rigid closure. This reduction in income, however, may be partially compensated for by benefits gained by Florida residents in the form of environmental services resulting from the adoption of silvopasture practices. As explained in chapter 3, there is no direct market for the environmental services of silvopasture; therefore, an alternative means of valuing these benefits must be utilized in order to compare their benefits in monetary terms. We utilized the WTP estimate of $924.4 from the Lake Okeechobee watershed from Shrestha and Alavalapati (2004) as a conservative estimate of the statewide WTP of Floridians for these services. When

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46 converting this value to a yearly benefit contribution, we calculated an annual benefit value of $46.22million. Utilizing the costs from the decrease in the income of households and the above calculated benefit value we estimated the total net benefit below. Table 5-7. Estimated costs and benefits of providing silvopasture Wage-flexible Wage-rigid Watershed WTP estimate (Benefit) 46.22 46.22 Income change (Cost) -22.16 -330.16 24.06 -283.94 Table 5-7 compares willingness to pay for the environmental services of silvopasture with the costs in terms of the income change that households were subjected to under each of the two model closures. This comparison shows that the benefits outweigh the costs by $24.06 million for wage-flexible scenario, but that the costs exceed the benefits by $284 million under the wage-rigid scenario. We use an income elasticity of demand of 1.0 to evaluate the change in demand for the environmental services provided by silvopasture. The change in income of Floridians was observed to be -0.0092% and -0.1374% for the wage-flexible and wage-rigid closures respectively. Since we utilize the WTP estimates for the environmental services of silvopasture in place of the demand for this non-market good, the estimates need to be adjusted for the change in income experienced by households in Florida. Although household incomes in Florida were decreased by millions of dollars under both closures, this amount is actually a very small percentage of overall household income and therefore, the adjusted WTP estimates differ very little form the previous estimates. The equations for the change in willingness to pay for the wage-flexible and wage-rigid scenarios are presented next.

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47 000092.0flexflexflexWTPIWTP and, 001374.0 rigidrigidrigidWTPIWTP Assuming the income elasticity as 1.0, these equations yield. and, )0.1)(000092.0(flexWTP )0.1)(001374.0( rigidWTP and, 000092.0flexWTP 001374.0 rigidWTP By multiplying these deltas by the original WTP and subtracting the output from the original estimates yields the income adjusted values in table 4-8. Table 5-8. Income adjusted changes in WTP estimates Wage-flexible Wage-rigid Income adjusted WTP estimate $46.2157 $46.1565 Base WTP estimate $46.2200 $46.2200 Change -$0.0043 $0.0635 We arrive at Table 5-9 by substituting these new values into the cost-benefit analysis. Table 5-9. Income adjusted estimated costs and benefits of silvopasture Wage-flexible Wage-rigid WTP estimate (Benefit) 46.216 46.156 Income change (Cost) -22.160 -330.160 24.056 -284.004 These results show that for the wage-flexible closure scenario, the benefits outweigh the costs by $24.056million. However, for the wage-rigid closure scenario, the costs exceed the benefits by $284.004million. These results present evidence that silvopasture will not necessarily give a positive benefit to society under all closure scenarios. This leads to some important questions. If the wage-rigid scenario is a more short term of the interactions in the economy, how long is the duration of these more intense negative impacts? How long will it take before industries make adjustments to their wage rates? Questions such as these could possibly be answered by constructing

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48 more advanced CGE models. Although these results do not currently provide conclusive evidence that the overall benefits to households will exceed the costs, it does show the possibility that they could. The above findings provide grounds for the need for more research in this area with more complex and detailed CGE models capable of adequately handling these issues.

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CHAPTER 6 SUMMARY, IMPLICATIONS, AND RESEARCH OPPORTUNITIES Summary In our study, we used a five sector CGE model of Florida to analyze the impacts to the economy of Florida in response to shocks simulating the adoption of silvopasture by all cattle ranchers in the state. We wanted to answer two questions. Primarily, we wanted to know how the modeled policy changes would impact Floridas economy. We analyzed this question under both a flexible and fixed wage enclosure and found that the incomes of Floridians would decrease by $22.16 million for the wage-flexible closure and $330.16 million under the wage-rigid closure. In addition, under the fixed wage enclosure scenario, we estimated that 5,322 Floridians would lose their employment. The cattle-ranching sector is found to lose approximately 3.0% or $5.6million as a result of the shocks. This decrease in sector activity is small when compared to the magnitude of the imposed shocks on that sector since ranchers pass on the higher costs of business to the manufacturing sector, which eventually results in higher beef prices for consumers. We also wanted to answer the question of how the welfare of Floridians would change as a result of the policy shocks when the environmental benefits of the policies are taken into account. Utilizing a cost benefit approach, we found that under the wage-flexible closure, households in Florida would come out ahead under the flexible wage scenario by $24.056million. However, under the wage fixed scenario, they would be worse off by $284.004million. 49

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50 Policy Implications The two different scenarios paint different pictures for policy makers of the possible severity of the impacts having ranchers in Florida adopt silvopasture. Since scenarios modeled with rigid wages reflect a shorter term than flexible wage scenarios, the actual response by the economy might be that first the economy reaches an equilibrium more closely in line with the wage-rigid scenario, and then over time moves towards the flexible wage scenario. This might imply that over the long run, the public will be better off in response to Floridian ranchers having to adopt silvopasture. However, policy makers are understandably reluctant to enact policies that will cause a large number of their constituent voters to lose their employment. This might imply to policy makers that they should employ policies that would not cause the entire state to adopt silvopasture at once. Figure 6-1. Difference in profitability for cattle-ranchers in Florida

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51 Although smaller incremental policies that required only certain areas to adopt silvopasture would certainly decrease the total economic impact to Florida, they would have much different effects on the local cattle ranchers. This can be shown from Figure 6-1. If policy makers implemented policies that affected all of the states cattle ranchers, then price will increase from P0 to P1 as quantity dropped as a result of the higher cost of producing the cattle under silvopasture. If only a small percentage of ranchers were affected at a time however, they would act as price takers, and the price may not rise. This would reduce the profits of the ranchers practicing silvopasture as they would no longer be able to capture the pink shaded area as part of their revenues and would only receive the blue shaded area representing revenues from the reduced quantity at the original price. This might imply that policy makers will have to develop policies such as tax incentives or carbon sequestration payments that would compensate the ranchers who are forced to adopt silvopasture for the environmental services they produce since they would be unable to pass their increased costs on to consumers through higher cattle prices. Model Limitations Because of the static nature of our model, it does not show the reactions of the economy in response to the shocks over time. Information on how the economy behaves in the transition period from the initial shock to the equilibrium point would be useful to policy makers. Another limitation is that we employed relatively simplistic Cobb-Douglas utility and production functions in the model. Other functional forms might improve how accurately the model reflects the behavior of economic agents in the real world. Changes such as these also greatly increase the data requirements of CGE models. Also, this model is constructed with data aggregated into only five sectors. With a greater

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52 degree of sector disaggregation, the number of possible policy shocks that this model could capture would increase. Also, the impacts of adopting silvopasture in Florida could be more accurately modeled. Also, we did not include economic returns from timber sales as part of the economic gain to ranchers because the returns would be received far in the future relative to the models yearly data and including a present value of these gains might present a distorted picture of the benefits ranchers actually observe. In addition to these limitations, the model results are highly affected by the allocations of costs described by the database that we used. The model is particularly sensitive to changes in the costs of the factors of production to which the shocks to the model were applied. It is possible that the proportion of factor costs associated with labor for the cattle sector in our model were overestimated while the proportion of factor costs associated with land rental costs were underestimated. In order to show how the results of the model could vary with a change in these factor cost proportions, we utilized an ufinished cow-calf budget, provided by Dr. Anton in the Food and Resource Economics Department at the Univeristy of Florida, to estimate the costs in labor for the cattle industry at $24 million annually. We then adjusted the original database values accordingly and ran the model again for both the wage-flexible and wage-rigid scenarios. The results for the model after these changes were made are provided in Appendix B. Under the wage-flexible scenario, this change caused the impact to Floridian household income to increase to $96 million from the $22 million estimated loss using the original model data. This larger drop can be attributed to the increased impact of the shock to the land base in the cattle sector as the proportion of that lands cost contribution increases. Similarly, the impact to the cattle sector increased as well from a

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53 $5.5 million decrease in industry demand to a $22.9 million drop after changing the model data. The increase in the impacts of the shocks as a result of changing the database input data were felt much more severly under the wage-rigid scenario. Under this scenario, the impacts on household income increased from a $330 million decrease to a drop of over $1.4 billion. The change in the impact to the cattle sector was comparable to the change under the wage-flexible scenario with the new data, with a decrease in sector demand of $23.1 million. These changes to the original database provide one extreme reference point to how the model would be impacted by changing the data for the cost allocations within the cattle sector. In order to provide an additional point of reference, we tripled the original cost of the land factor for the model and redistributed the remaining factor costs to the labor factor of production. The effects of these data changes were between the other two scenarios and the full list model results with the new data as an imput to the model are listed in Appendix C. Under the wage-flexible scenario, the impact to household income for Floridians increased from the $22 million loss in the original scenario to a loss of almost $43 million. The model showed that the impact to cattle sector demand would also increase to $10 million from $5.5million. Under the fixed-wage scenario, the increase in the household income impact increased by a similar scale from $330 million to $635 million. Similar to the decrease in cattle sector demand under the wage-flexible scenario, the impact to the demand from the cattle sector the under wage-fixed scenario also increased to about $10 million.

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54 These large variations in the output, resulting from changing the input data for the cattle sector, demonstrate one limitation of this model in its sensitivity to this type of change. As a result of this limitation, it is important that research is conducted to gather data specifically on the cost strucutres of the land utilizing portion of the cattle ranching industry in Florida. Increasing the accuracy of the data utilized to represent the cost structure of the cattle industry in this CGE model would have a great impact on the ability of the model to accurately determine economic impacts of shocks to the Florida economy. The results of the output changes after changing the input data were discussed to show the sensitivity of the model to this type of change, hence the results were not discussed in the same level of detail as the original model. However, the full model output sets for each of the two changes are provided in Appendicies B and C. Research Opportunities There are several areas for future research opportunities relating to this study. First of all, a dynamic model, which has the ability to analyze the economic impacts to policy shocks related to silvopasture with respect to time, could be investigated. This would give policy makers more information on the time it would take for the effects to be felt by the economy. It would also give more information on the smoothness of the transitions experienced in the economy over time. Also, equations that capture the impacts that the policy changes would have on the environment as well as environmental feedbacks into the economy could be introduced into the CGE model. At the same time, a model could be developed based on a utility function incorporating environmental variables as part of the welfare of households, providing a more robust analysis of the well being of households as a result of the sum of the economic and environmental changes they experience.

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APPENDIX A MODEL OUTPUT Wage-flexible Scenario Output SETS ---No Name Size Description -------------------------------------1 SECT 5 Sectors 2 FAC 3 Factors 3 NUM_SECT 1 sector 1 4 TWO_SECT 4 sectors 2-5 5 NSEC_SECT 1 sector 2 6 FORE_SECT 1 sector 3 7 LAB_FAC 1 Labor Factor of Production, factor 1 8 KD_FAC 2 Capital and Land Factors, factors 2&3 9 LAND_FAC 1 Land Factor of Production, factor 3 VARIABLES --------No Name Size Arguments (if any) and Description ---------------------------------------------------------------1 p_Y 1 Total household expenditure 2 p_PC 5 (SECT) Price of commodities 3 p_PFL 1 (LAB_FAC) Price of Labor 4 p_PF 10 (KD_FAC,SECT) Price of factors 5 p_XCOM 5 (SECT) Total demand for (or supply of) commod ... 6 p_XFACL 1 (LAB_FAC) Total demand for (or supply of) factors 7 p_XFACKD 2 (KD_FAC) Total demand for (or supply of) factors 8 p_XH 5 (SECT) Household consumption of commodities 9 p_XC 25 (SECT,SECT) Intermediate commodity inputs 10 p_XFL 5 (LAB_FAC,SECT) Intermediate factor inputs 11 p_XFKD 10 (KD_FAC,SECT) Intermediate factor inputs 12 p_XEXP 5 (SECT) Net Exports of commodities 13 p_PW 5 (SECT) World Price of Commodity 14 p_TLF 1 Total Labor Force 15 p_ULF 1 Unemployed Labor Force 16 p_T 1 Consumer Market Tax TOTAL NUMBER OF exogenous VARIABLES IS 18. THEY ARE AS FOLLOWS: Just 5 of the 10 components of 'p_PF' -namely components: 1, 3, 5, 7, 9 All 1 components of 'p_XFACL' Just 5 of the 10 components of 'p_XFKD' -namely components: 2, 4, 6, 8, 10 All 5 components of 'p_PW' All 1 components of 'p_TLF' All 1 components of 'p_T' TOTAL NUMBER OF endogenous VARIABLES IS 65. THEY ARE AS FOLLOWS: 55

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56 All 1 components of 'p_Y' All 5 components of 'p_PC' All 1 components of 'p_PFL' Just 5 of the 10 components of 'p_PF' -namely components: 2, 4, 6, 8, 10 All 5 components of 'p_XCOM' All 2 components of 'p_XFACKD' All 5 components of 'p_XH' All 25 components of 'p_XC' All 5 components of 'p_XFL' Just 5 of the 10 components of 'p_XFKD' -namely components: 1, 3, 5, 7, 9 All 5 components of 'p_XEXP' All 1 components of 'p_ULF' TOTAL NUMBER OF shocked VARIABLES IS 2. THEY ARE AS FOLLOWS: Just 1 of the 10 components of 'p_PF' -namely components: 1 Just 1 of the 10 components of 'p_XFKD' -namely components: 2 THE SHOCKS ARE AS FOLLOWS. p_PF 1 SHOCK = 25.000000 p_XFKD 2 SHOCK = -25.000000 END OF THE SHOCKS. ALL THE endogenous VARIABLES ARE cumulatively-retained endogenous. SHOCKS RELEVANT TO THE PRINT-OUT BELOW p_PF 1 SHOCK = 25.000000 p_XFKD 2 SHOCK = -25.000000 THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE. When levels values are available for a variable, they are shown underneath the percent-change or change result. The 4 results are shown in the order: Percent-change (or change), Pre-simulation, Post-simulation, Change. For example 3.000 (percent change) 500.0 (pre-sim level) 515.0 (post-sim level) 15.0 (change) p_Y Total household expenditure -0.009218 240336.562000 240314.406000 -22.156250

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57 p_PC (SECT) Price of commodities s1 s2 s3 s4 s5 3.034190 0.000000* -0.000819 0.000595 -0.004364 1.000000 1.000000 1.000000 1.000000 1.000000 1.030342 1.000000 0.999992 1.000006 0.999956 0.030342 0.000000* -0.000008 0.000006 -0.000044 p_PFL (LAB_FAC) Price of Labor labor -0.006444 1.000000 0.999936 -0.000064 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s1) results where '-' is in set 'KD_FAC'. land 33.238327 1.000000 1.332383 0.332383 p_PF(-,s2) results where '-' is in set 'KD_FAC'. land -0.002865 1.000000 0.999971 -0.000029 p_PF(-,s3) results where '-' is in set 'KD_FAC'. land -0.000080 1.000000 0.999999 -0.000001 p_PF(-,s4) results where '-' is in set 'KD_FAC'. land -0.004617 1.000000 0.999954 -0.000046 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s5) results where '-' is in set 'KD_FAC'. land -0.006683 1.000000 0.999933 -0.000067 p_XCOM (SECT) Total demand for (or supply of) commodities s1 s2 s3 s4 s5 -2.947586 -0.002865 0.000739 -0.005211 -0.002319

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58 188.559998 7694.735350 405.838989 54213.156200 416915.562000 183.002029 7694.515140 405.841980 54210.332000 416905.906000 -5.557968 -0.220215 0.002991 -2.824219 -9.656250 p_XFACKD (KD_FAC) Total demand for (or supply of) factors capital land -0.008778 -0.245993 102767.062000 828.379639 102758.039000 826.341858 -9.023438 -2.037781 p_XH (SECT) Household consumption of commodities s1 s2 s3 s4 s5 -2.954021 -0.009218 -0.008400 -0.009813 -0.004854 0.000000* 1126.350950 1.897000 17089.293000 222119.016000 0.000000* 1126.247070 1.896841 17087.615200 222108.234000 0.000000* -0.103882 -0.000159 -1.677734 -10.781250 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s1) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -2.947586 -0.002589 -0.001770 -0.003183 0.001776 41.120998 7.355000 0.000000* 3.335000 43.889000 39.908920 7.354810 0.000000* 3.334894 43.889778 -1.212078 -0.000190 0.000000* -0.000106 0.000778 p_XC(-,s2) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -2.947854 -0.002865 -0.002046 -0.003459 0.001500 9.930000 870.853027 0.087000 303.221008 1131.000000 9.637279 870.828064 0.086998 303.210510 1131.016970 -0.292722 -0.024963 -0.000002 -0.010498 0.016968 p_XC(-,s3) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -2.945151 -0.000080 0.000739 -0.000674 0.004285 0.000000* 122.128998 0.315000 5.378000 52.792999 0.000000* 122.128899 0.315002 5.377964 52.795261 0.000000* -0.000099 0.000002 -0.000036 0.002262 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s4) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -2.949555 -0.004617 -0.003798 -0.005211 -0.000252 76.509003 748.979004 3.179000 8959.433590 15060.677700 74.252327 748.944397 3.178879 8958.966800 15060.639600 -2.256676 -0.034607 -0.000121 -0.466797 -0.038086 p_XC(-,s5) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -2.951561 -0.006683 -0.005864 -0.007278 -0.002319 0.000000* 849.520996 0.000000* 7559.000000 99637.156200 0.000000* 849.464233 0.000000* 7558.449710 99634.843800 0.000000* -0.056763 0.000000* -0.550293 -2.312500 p_XFL (LAB_FAC,SECT) Intermediate factor inputs

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59 p_XFL(-,s1) results where '-' is in set 'LAB_FAC'. labor 0.003855 75.111000 75.113899 0.002899 p_XFL(-,s2) results where '-' is in set 'LAB_FAC'. labor 0.003579 3541.367920 3541.494630 0.126709 p_XFL(-,s3) results where '-' is in set 'LAB_FAC'. labor 0.006364 15.223000 15.223969 0.000969 p_XFL(-,s4) results where '-' is in set 'LAB_FAC'. labor 0.001827 21113.478500 21113.863300 0.384766 p_XFL(-,s5) results where '-' is in set 'LAB_FAC'. labor -0.000240 215591.422000 215590.906000 -0.515625 p_XFKD (KD_FAC,SECT) Intermediate factor inputs p_XFKD(-,s1) results where '-' is in set 'KD_FAC'. capital -20.031319 10.638600 8.507548 -2.131052 p_XFKD(-,s2) results where '-' is in set 'KD_FAC'. capital -0.002865 1102.939940 1102.908330 -0.031616 p_XFKD(-,s3) results where '-' is in set 'KD_FAC'. capital -0.000080 125.987000 125.986900

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60 -0.000099 p_XFKD(-,s4) results where '-' is in set 'KD_FAC'. capital -0.004617 8250.000000 8249.619140 -0.380859 p_XFKD(-,s5) results where '-' is in set 'KD_FAC'. capital -0.006683 93277.500000 93271.265600 -6.234375 p_XEXP (SECT) Net Exports of commodities s1 s2 s3 s4 s5 -2.945074 0.000000* 0.000819 -0.000595 0.004365 61.000000 3969.547120 400.360992 20293.494100 78871.039100 59.203506 3969.547120 400.364258 20293.373000 78874.484400 -1.796494 0.000000* 0.003265 -0.121094 3.445312 p_ULF Unemployed Labor Force 0.000000* 3.900000 3.900000 0.000000* Wage-rigid Scenario Output SETS ---No Name Size Description -------------------------------------1 SECT 5 Sectors 2 FAC 3 Factors 3 NUM_SECT 1 sector 1 4 TWO_SECT 4 sectors 2-5 5 NSEC_SECT 1 sector 2 6 FORE_SECT 1 sector 3 7 LAB_FAC 1 Labor Factor of Production, factor 1 8 KD_FAC 2 Capital and Land Factors, factors 2&3 9 LAND_FAC 1 Land Factor of Production, factor 3 VARIABLES --------No Name Size Arguments (if any) and Description ---------------------------------------------------------------1 p_Y 1 Total household expenditure 2 p_PC 5 (SECT) Price of commodities 3 p_PFL 1 (LAB_FAC) Price of Labor 4 p_PF 10 (KD_FAC,SECT) Price of factors 5 p_XCOM 5 (SECT) Total demand for (or supply of) commod ... 6 p_XFACL 1 (LAB_FAC) Total demand for (or supply of) factors 7 p_XFACKD 2 (KD_FAC) Total demand for (or supply of) factors 8 p_XH 5 (SECT) Household consumption of commodities 9 p_XC 25 (SECT,SECT) Intermediate commodity inputs

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61 10 p_XFL 5 (LAB_FAC,SECT) Intermediate factor inputs 11 p_XFKD 10 (KD_FAC,SECT) Intermediate factor inputs 12 p_XEXP 5 (SECT) Net Exports of commodities 13 p_PW 5 (SECT) World Price of Commodity 14 p_TLF 1 Total Labor Force 15 p_ULF 1 Unemployed Labor Force 16 p_T 1 Consumer Market Tax THE SHOCKS ARE AS FOLLOWS. p_PF 1 SHOCK = 25.000000 p_XFKD 2 SHOCK = -25.000000 END OF THE SHOCKS. THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE. p_Y Total household expenditure -0.137372 240336.562000 240006.406000 -330.156250 p_PC (SECT) Price of commodities s1 s2 s3 s4 s5 3.037269 0.000000* -0.000163 0.005098 0.000121 1.000000 1.000000 1.000000 1.000000 1.000000 1.030373 1.000000 0.999998 1.000051 1.000001 0.030373 0.000000* -0.000002 0.000051 0.000001 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s1) results where '-' is in set 'KD_FAC'. land 33.190601 1.000000 1.331906 0.331906 p_PF(-,s2) results where '-' is in set 'KD_FAC'. land -0.042688 1.000000 0.999573 -0.000427 p_PF(-,s3) results where '-' is in set 'KD_FAC'. land -0.001191 1.000000 0.999988 -0.000012 p_PF(-,s4) results where '-' is in set 'KD_FAC'.

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62 land -0.068801 1.000000 0.999312 -0.000688 p_PF(-,s5) results where '-' is in set 'KD_FAC'. land -0.099595 1.000000 0.999004 -0.000996 p_XCOM (SECT) Total demand for (or supply of) commodities s1 s2 s3 s4 s5 -2.985418 -0.042688 -0.001028 -0.073895 -0.099716 188.559998 7694.735350 405.838989 54213.156200 416915.562000 182.930695 7691.450680 405.834808 54173.093800 416499.844000 -5.629303 -3.284668 -0.004181 -40.062500 -415.718750 p_XFACL (LAB_FAC) Total demand for (or supply of) factors labor -0.096026 240336.594000 240105.812000 -230.781250 p_XFACKD (KD_FAC) Total demand for (or supply of) factors capital land -0.098693 -0.245993 102767.062000 828.379639 102665.641000 826.341858 -101.421875 -2.037781 p_XH (SECT) Household consumption of commodities s1 s2 s3 s4 s5 -3.081308 -0.137372 -0.137209 -0.142463 -0.137493 0.000000* 1126.350950 1.897000 17089.293000 222119.016000 0.000000* 1124.803710 1.894397 17064.947300 221813.609000 0.000000* -1.547241 -0.002603 -24.345703 -305.406250 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s1) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -2.985418 -0.038578 -0.038414 -0.043673 -0.038699 41.120998 7.355000 0.000000* 3.335000 43.889000 39.893364 7.352163 0.000000* 3.333544 43.872017 -1.227634 -0.002837 0.000000* -0.001456 -0.016983 p_XC(-,s2) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -2.989408 -0.042688 -0.042525 -0.047783 -0.042809 9.930000 870.853027 0.087000 303.221008 1131.000000 9.633152 870.481262 0.086963 303.076111 1130.515870 -0.296848 -0.371765 -0.000037 -0.144897 -0.484131

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63 p_XC(-,s3) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -2.949130 -0.001191 -0.001028 -0.006288 -0.001312 0.000000* 122.128998 0.315000 5.378000 52.792999 0.000000* 122.127541 0.314997 5.377662 52.792305 0.000000* -0.001457 -0.000003 -0.000338 -0.000694 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s4) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -3.014753 -0.068801 -0.068638 -0.073895 -0.068922 76.509003 748.979004 3.179000 8959.433590 15060.677700 74.202446 748.463684 3.176818 8952.813480 15050.297900 -2.306557 -0.515320 -0.002182 -6.620117 -10.379883 p_XC(-,s5) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -3.044641 -0.099595 -0.099432 -0.104687 -0.099716 0.000000* 849.520996 0.000000* 7559.000000 99637.156200 0.000000* 848.674927 0.000000* 7551.086910 99537.804700 0.000000* -0.846069 0.000000* -7.913086 -99.351563 p_XFL (LAB_FAC,SECT) Factor inputs p_XFL(-,s1) results where '-' is in set 'LAB_FAC'. labor -0.038578 75.111000 75.082024 -0.028976 p_XFL(-,s2) results where '-' is in set 'LAB_FAC'. labor -0.042688 3541.367920 3539.856200 -1.511719 p_XFL(-,s3) results where '-' is in set 'LAB_FAC'. labor -0.001191 15.223000 15.222818 -0.000181 p_XFL(-,s4) results where '-' is in set 'LAB_FAC'. labor -0.068801 21113.478500 21098.953100 -14.525391 p_XFL(-,s5) results where '-' is in set 'LAB_FAC'. labor -0.099595 215591.422000

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64 215376.703000 -214.718750 p_XFKD (KD_FAC,SECT) Factor inputs p_XFKD(-,s1) results where '-' is in set 'KD_FAC'. capital -20.060226 10.638600 8.504473 -2.134128 p_XFKD(-,s2) results where '-' is in set 'KD_FAC'. capital -0.042688 1102.939940 1102.469120 -0.470825 p_XFKD(-,s3) results where '-' is in set 'KD_FAC'. capital -0.001191 125.987000 125.985497 -0.001503 p_XFKD(-,s4) results where '-' is in set 'KD_FAC'. capital -0.068801 8250.000000 8244.324220 -5.675781 p_XFKD(-,s5) results where '-' is in set 'KD_FAC'. capital -0.099595 93277.500000 93184.601600 -92.898438 p_XEXP (SECT) Net Exports of commodities s1 s2 s3 s4 s5 -2.947974 0.000000* 0.000163 -0.005097 -0.000121 61.000000 3969.547120 400.360992 20293.494100 78871.039100 59.201736 3969.547120 400.361633 20292.459000 78870.945300 -1.798264 0.000000* 0.000641 -1.035156 -0.093750 p_ULF Unemployed Labor Force 1.842080 3.900000 3.971841 0.071841

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APPENDIX B ADJUSTED DATA SET MODEL OUTPUT ONE Wage-flexible Scenario Output SETS ---No Name Size Description -------------------------------------1 SECT 5 Sectors 2 FAC 3 Factors 3 NUM_SECT 1 sector 1 4 TWO_SECT 4 sectors 2-5 5 NSEC_SECT 1 sector 2 6 FORE_SECT 1 sector 3 7 LAB_FAC 1 Labor Factor of Production, factor 1 8 KD_FAC 2 Capital and Land Factors, factors 2&3 9 LAND_FAC 1 Land Factor of Production, factor 3 VARIABLES --------No Name Size Arguments (if any) and Description ---------------------------------------------------------------1 p_Y 1 Total household expenditure 2 p_PC 5 (SECT) Price of commodities 3 p_PFL 1 (LAB_FAC) Price of Labor 4 p_PF 10 (KD_FAC,SECT) Price of factors 5 p_XCOM 5 (SECT) Total demand for (or supply of) commod ... 6 p_XFACL 1 (LAB_FAC) Total demand for (or supply of) factors 7 p_XFACKD 2 (KD_FAC) Total demand for (or supply of) factors 8 p_XH 5 (SECT) Household consumption of commodities 9 p_XC 25 (SECT,SECT) Intermediate commodity inputs 10 p_XFL 5 (LAB_FAC,SECT) Intermediate factor inputs 11 p_XFKD 10 (KD_FAC,SECT) Intermediate factor inputs 12 p_XEXP 5 (SECT) Net Exports of commodities 13 p_PW 5 (SECT) World Price of Commodity 14 p_TLF 1 Total Labor Force 15 p_ULF 1 Unemployed Labor Force 16 p_T 1 Consumer Market Tax THE SHOCKS ARE AS FOLLOWS. p_PF 1 SHOCK = 25.000000 p_XFKD 2 SHOCK = -25.000000 END OF THE SHOCKS. ALL THE endogenous VARIABLES ARE cumulatively-retained endogenous. 65

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66 SHOCKS RELEVANT TO THE PRINT-OUT BELOW p_PF 1 SHOCK = 25.000000 p_XFKD 2 SHOCK = -25.000000 THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE. p_Y Total household expenditure -0.039933 240336.562000 240240.594000 -95.968750 p_PC (SECT) Price of commodities s1 s2 s3 s4 s5 13.819850 0.000000* -0.003548 0.002576 -0.018910 1.000000 1.000000 1.000000 1.000000 1.000000 1.138198 1.000000 0.999965 1.000026 0.999811 0.138198 0.000000* -0.000035 0.000026 -0.000189 p_PFL (LAB_FAC) Price of Labor labor -0.027917 1.000000 0.999721 -0.000279 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s1) results where '-' is in set 'KD_FAC'. land 33.226902 1.000000 1.332269 0.332269 p_PF(-,s2) results where '-' is in set 'KD_FAC'. land -0.012409 1.000000 0.999876 -0.000124 p_PF(-,s3) results where '-' is in set 'KD_FAC'. land -0.000346 1.000000 0.999997 -0.000003 p_PF(-,s4) results where '-' is in set 'KD_FAC'.

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67 land -0.020000 1.000000 0.999800 -0.000200 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s5) results where '-' is in set 'KD_FAC'. land -0.028951 1.000000 0.999711 -0.000289 p_XCOM (SECT) Total demand for (or supply of) commodities s1 s2 s3 s4 s5 -12.159436 -0.012409 0.003201 -0.022575 -0.010044 188.559998 7694.735350 405.838989 54213.156200 416915.562000 165.632172 7693.780270 405.851990 54200.918000 416873.688000 -22.927826 -0.955078 0.013000 -12.238281 -41.875000 p_XFACKD (KD_FAC) Total demand for (or supply of) factors capital land -0.030328 -1.885813 102767.062000 879.490234 102735.898000 862.904663 -31.164062 -16.585571 p_XH (SECT) Household consumption of commodities s1 s2 s3 s4 s5 -12.184669 -0.039933 -0.036387 -0.042507 -0.021027 0.000000* 1126.350950 1.897000 17089.293000 222119.016000 0.000000* 1125.901120 1.896310 17082.029300 222072.312000 0.000000* -0.449829 -0.000690 -7.263672 -46.703125 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s1) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.159436 -0.011214 -0.007667 -0.013789 0.007697 41.120998 7.355000 0.000000* 3.335000 43.889000 36.120918 7.354175 0.000000* 3.334540 43.892380 -5.000080 -0.000825 0.000000* -0.000460 0.003380 p_XC(-,s2) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.160485 -0.012409 -0.008862 -0.014984 0.006501 9.930000 870.853027 0.087000 303.221008 1131.000000 8.722464 870.744934 0.086992 303.175568 1131.073490 -1.207537 -0.108093 -0.000008 -0.045441 0.073486 p_XC(-,s3) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.149884 -0.000346 0.003201 -0.002922 0.018567 0.000000* 122.128998 0.315000 5.378000 52.792999 0.000000* 122.128578 0.315010 5.377842 52.802803

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68 0.000000* -0.000420 0.000010 -0.000157 0.009804 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s4) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.167155 -0.020000 -0.016453 -0.022575 -0.001091 76.509003 748.979004 3.179000 8959.433590 15060.677700 67.200035 748.829224 3.178477 8957.411130 15060.513700 -9.308968 -0.149780 -0.000523 -2.022461 -0.164062 p_XC(-,s5) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.175018 -0.028951 -0.025405 -0.031526 -0.010044 0.000000* 849.520996 0.000000* 7559.000000 99637.156200 0.000000* 849.275024 0.000000* 7556.616700 99627.148400 0.000000* -0.245972 0.000000* -2.383301 -10.007812 p_XFL (LAB_FAC,SECT) Intermediate factor inputs p_XFL(-,s1) results where '-' is in set 'LAB_FAC'. labor 0.016708 24.000000 24.004009 0.004009 p_XFL(-,s2) results where '-' is in set 'LAB_FAC'. labor 0.015512 3541.367920 3541.917240 0.549316 p_XFL(-,s3) results where '-' is in set 'LAB_FAC'. labor 0.027579 15.223000 15.227198 0.004198 p_XFL(-,s4) results where '-' is in set 'LAB_FAC'. labor 0.007920 21113.478500 21115.150400 1.671875 p_XFL(-,s5) results where '-' is in set 'LAB_FAC'. labor -0.001034 215591.422000 215589.188000 -2.234375 p_XFKD (KD_FAC,SECT) Intermediate factor inputs p_XFKD(-,s1) results where '-' is in set 'KD_FAC'.

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69 capital -20.038233 10.638600 8.506813 -2.131787 p_XFKD(-,s2) results where '-' is in set 'KD_FAC'. capital -0.012409 1102.939940 1102.803100 -0.136841 p_XFKD(-,s3) results where '-' is in set 'KD_FAC'. capital -0.000346 125.987000 125.986565 -0.000435 p_XFKD(-,s4) results where '-' is in set 'KD_FAC'. capital -0.020000 8250.000000 8248.349610 -1.650391 p_XFKD(-,s5) results where '-' is in set 'KD_FAC'. capital -0.028951 93277.500000 93250.492200 -27.007812 p_XEXP (SECT) Net Exports of commodities s1 s2 s3 s4 s5 -12.149580 0.000000* 0.003548 -0.002575 0.018913 61.000000 3969.547120 400.360992 20293.494100 78871.039100 53.588757 3969.547120 400.375183 20292.970700 78885.953100 -7.411243 0.000000* 0.014191 -0.523438 14.914062 Wage-fixed Scenario Output SETS ---No Name Size Description -------------------------------------1 SECT 5 Sectors 2 FAC 3 Factors 3 NUM_SECT 1 sector 1 4 TWO_SECT 4 sectors 2-5 5 NSEC_SECT 1 sector 2 6 FORE_SECT 1 sector 3 7 LAB_FAC 1 Labor Factor of Production, factor 1 8 KD_FAC 2 Capital and Land Factors, factors 2&3 9 LAND_FAC 1 Land Factor of Production, factor 3

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70 VARIABLES --------No Name Size Arguments (if any) and Description ---------------------------------------------------------------1 p_Y 1 Total household expenditure 2 p_PC 5 (SECT) Price of commodities 3 p_PFL 1 (LAB_FAC) Price of Labor 4 p_PF 10 (KD_FAC,SECT) Price of factors 5 p_XCOM 5 (SECT) Total demand for (or supply of) commod ... 6 p_XFACL 1 (LAB_FAC) Total demand for (or supply of) factors 7 p_XFACKD 2 (KD_FAC) Total demand for (or supply of) factors 8 p_XH 5 (SECT) Household consumption of commodities 9 p_XC 25 (SECT,SECT) Intermediate commodity inputs 10 p_XFL 5 (LAB_FAC,SECT) Intermediate factor inputs 11 p_XFKD 10 (KD_FAC,SECT) Intermediate factor inputs 12 p_XEXP 5 (SECT) Net Exports of commodities 13 p_PW 5 (SECT) World Price of Commodity 14 p_TLF 1 Total Labor Force 15 p_ULF 1 Unemployed Labor Force 16 p_T 1 Consumer Market Tax THE SHOCKS ARE AS FOLLOWS. p_PF 1 SHOCK = 25.000000 p_XFKD 2 SHOCK = -25.000000 END OF THE SHOCKS. THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE. p_Y Total household expenditure -0.591877 240336.562000 238914.062000 -1422.500000 p_PC (SECT) Price of commodities s1 s2 s3 s4 s5 13.762493 0.000000* -0.000703 0.021981 0.000522 1.000000 1.000000 1.000000 1.000000 1.000000 1.137625 1.000000 0.999993 1.000220 1.000005 0.137625 0.000000* -0.000007 0.000220 0.000005 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s1) results where '-' is in set 'KD_FAC'. land 33.021626 1.000000 1.330216 0.330216

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71 p_PF(-,s2) results where '-' is in set 'KD_FAC'. land -0.183923 1.000000 0.998161 -0.001839 p_PF(-,s3) results where '-' is in set 'KD_FAC'. land -0.005132 1.000000 0.999949 -0.000051 p_PF(-,s4) results where '-' is in set 'KD_FAC'. land -0.296434 1.000000 0.997036 -0.002964 p_PF(-,s5) results where '-' is in set 'KD_FAC'. land -0.429110 1.000000 0.995709 -0.004291 p_XCOM (SECT) Total demand for (or supply of) commodities s1 s2 s3 s4 s5 -12.251341 -0.183923 -0.004429 -0.318346 -0.429630 188.559998 7694.735350 405.838989 54213.156200 416915.562000 165.458862 7680.583010 405.821014 54040.570300 415124.375000 -23.101135 -14.152344 -0.017975 -172.585938 -1791.187500 p_XFACL (LAB_FAC) Total demand for (or supply of) factors labor -0.413785 240285.500000 239291.234000 -994.265625 p_XFACKD (KD_FAC) Total demand for (or supply of) factors capital land -0.417583 -1.885812 102767.062000 879.490234 102337.922000 862.904724 -429.140625 -16.585510 p_XH (SECT) Household consumption of commodities s1 s2 s3 s4 s5 -12.625557 -0.591877 -0.591178 -0.613724 -0.592396 0.000000* 1126.350950 1.897000 17089.293000 222119.016000 0.000000* 1119.684330 1.885785 16984.412100 220803.188000

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72 0.000000* -6.666626 -0.011215 -104.880859 -1315.828120 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s1) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.251341 -0.166210 -0.165508 -0.188150 -0.166731 41.120998 7.355000 0.000000* 3.335000 43.889000 36.083126 7.342775 0.000000* 3.328725 43.815823 -5.037872 -0.012225 0.000000* -0.006275 -0.073177 p_XC(-,s2) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.266912 -0.183923 -0.183221 -0.205859 -0.184445 9.930000 870.853027 0.087000 303.221008 1131.000000 8.711896 869.251343 0.086841 302.596802 1128.913940 -1.218104 -1.601685 -0.000159 -0.624207 -2.086060 p_XC(-,s3) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.109735 -0.005132 -0.004429 -0.027107 -0.005654 0.000000* 122.128998 0.315000 5.378000 52.792999 0.000000* 122.122726 0.314986 5.376542 52.790012 0.000000* -0.006271 -0.000014 -0.001458 -0.002987 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s4) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.365824 -0.296434 -0.295733 -0.318346 -0.296955 76.509003 748.979004 3.179000 8959.433590 15060.677700 67.048035 746.758789 3.169599 8930.912110 15015.954100 -9.460968 -2.220215 -0.009401 -28.521484 -44.723633 p_XC(-,s5) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -12.482463 -0.429110 -0.428409 -0.450992 -0.429630 0.000000* 849.520996 0.000000* 7559.000000 99637.156200 0.000000* 845.875610 0.000000* 7524.909670 99209.085900 0.000000* -3.645386 0.000000* -34.090332 -428.070312 p_XFL (LAB_FAC,SECT) Intermediate factor inputs p_XFL(-,s1) results where '-' is in set 'LAB_FAC'. labor -0.166210 24.000000 23.960110 -0.039890 p_XFL(-,s2) results where '-' is in set 'LAB_FAC'. labor -0.183923 3541.367920 3534.854490 -6.513428 p_XFL(-,s3) results where '-' is in set 'LAB_FAC'.

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73 labor -0.005132 15.223000 15.222219 -0.000781 p_XFL(-,s4) results where '-' is in set 'LAB_FAC'. labor -0.296434 21113.478500 21050.890600 -62.587891 p_XFL(-,s5) results where '-' is in set 'LAB_FAC'. labor -0.429110 215591.422000 214666.297000 -925.125000 p_XFKD (KD_FAC,SECT) Intermediate factor inputs p_XFKD(-,s1) results where '-' is in set 'KD_FAC'. capital -20.162603 10.638600 8.493582 -2.145019 p_XFKD(-,s2) results where '-' is in set 'KD_FAC'. capital -0.183923 1102.939940 1100.911380 -2.028564 p_XFKD(-,s3) results where '-' is in set 'KD_FAC'. capital -0.005132 125.987000 125.980537 -0.006462 p_XFKD(-,s4) results where '-' is in set 'KD_FAC'. capital -0.296434 8250.000000 8225.543950 -24.456055 p_XFKD(-,s5) results where '-' is in set 'KD_FAC'. capital -0.429110 93277.500000 92877.234400

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74 -400.265625 p_XEXP (SECT) Net Exports of commodities s1 s2 s3 s4 s5 -12.105221 0.000000* 0.000703 -0.021976 -0.000522 61.000000 3969.547120 400.360992 20293.494100 78871.039100 53.615814 3969.547120 400.363800 20289.035200 78870.625000 -7.384186 0.000000* 0.002808 -4.458984 -0.414062 p_ULF Unemployed Labor Force 8.194535 3.900000 4.219587 0.319587

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APPENDIX C ADJUSTED DATA SET MODEL OUTPUT TWO Wage-flexible Scenario Output SETS ---No Name Size Description -------------------------------------1 SECT 5 Sectors 2 FAC 3 Factors 3 NUM_SECT 1 sector 1 4 TWO_SECT 4 sectors 2-5 5 NSEC_SECT 1 sector 2 6 FORE_SECT 1 sector 3 7 LAB_FAC 1 Labor Factor of Production, factor 1 8 KD_FAC 2 Capital and Land Factors, factors 2&3 9 LAND_FAC 1 Land Factor of Production, factor 3 VARIABLES --------No Name Size Arguments (if any) and Description ---------------------------------------------------------------1 p_Y 1 Total household expenditure 2 p_PC 5 (SECT) Price of commodities 3 p_PFL 1 (LAB_FAC) Price of Labor 4 p_PF 10 (KD_FAC,SECT) Price of factors 5 p_XCOM 5 (SECT) Total demand for (or supply of) commod ... 6 p_XFACL 1 (LAB_FAC) Total demand for (or supply of) factors 7 p_XFACKD 2 (KD_FAC) Total demand for (or supply of) factors 8 p_XH 5 (SECT) Household consumption of commodities 9 p_XC 25 (SECT,SECT) Intermediate commodity inputs 10 p_XFL 5 (LAB_FAC,SECT) Intermediate factor inputs 11 p_XFKD 10 (KD_FAC,SECT) Intermediate factor inputs 12 p_XEXP 5 (SECT) Net Exports of commodities 13 p_PW 5 (SECT) World Price of Commodity 14 p_TLF 1 Total Labor Force 15 p_ULF 1 Unemployed Labor Force 16 p_T 1 Consumer Market Tax THE SHOCKS ARE AS FOLLOWS. p_PF 1 SHOCK = 25.000000 p_XFKD 2 SHOCK = -25.000000 END OF THE SHOCKS. THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE. 75

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76 p_Y Total household expenditure -0.017755 240336.562000 240293.891000 -42.671875 p_PC (SECT) Price of commodities s1 s2 s3 s4 s5 5.925771 0.000000* -0.001577 0.001145 -0.008407 1.000000 1.000000 1.000000 1.000000 1.000000 1.059258 1.000000 0.999984 1.000011 0.999916 0.059258 0.000000* -0.000016 0.000011 -0.000084 p_PFL (LAB_FAC) Price of Labor labor -0.012412 1.000000 0.999876 -0.000124 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s1) results where '-' is in set 'KD_FAC'. land 33.235149 1.000000 1.332351 0.332351 p_PF(-,s2) results where '-' is in set 'KD_FAC'. land -0.005517 1.000000 0.999945 -0.000055 p_PF(-,s3) results where '-' is in set 'KD_FAC'. land -0.000154 1.000000 0.999998 -0.000002 p_PF(-,s4) results where '-' is in set 'KD_FAC'. land -0.008892 1.000000 0.999911 -0.000089 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s5) results where '-' is in set 'KD_FAC'. land -0.012873 1.000000

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77 0.999871 -0.000129 p_XCOM (SECT) Total demand for (or supply of) commodities s1 s2 s3 s4 s5 -5.600160 -0.005517 0.001423 -0.010037 -0.004466 188.559998 7694.735350 405.838989 54213.156200 416915.562000 178.000336 7694.311040 405.844757 54207.714800 416896.938000 -10.559662 -0.424316 0.005768 -5.441406 -18.625000 p_XFACKD (KD_FAC) Total demand for (or supply of) factors capital land -0.014767 -0.724580 102767.062000 842.587219 102751.883000 836.481995 -15.179688 -6.105225 p_XH (SECT) Household consumption of commodities s1 s2 s3 s4 s5 -5.612217 -0.017755 -0.016178 -0.018900 -0.009349 0.000000* 1126.350950 1.897000 17089.293000 222119.016000 0.000000* 1126.151000 1.896693 17086.062500 222098.250000 0.000000* -0.199951 -0.000307 -3.230469 -20.765625 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s1) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.600160 -0.004985 -0.003408 -0.006130 0.003422 41.120998 7.355000 0.000000* 3.335000 43.889000 38.818157 7.354633 0.000000* 3.334796 43.890503 -2.302841 -0.000367 0.000000* -0.000204 0.001503 p_XC(-,s2) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.600662 -0.005517 -0.003940 -0.006662 0.002890 9.930000 870.853027 0.087000 303.221008 1131.000000 9.373855 870.804993 0.086997 303.200806 1131.032710 -0.556146 -0.048035 -0.000003 -0.020203 0.032715 p_XC(-,s3) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.595598 -0.000154 0.001423 -0.001299 0.008253 0.000000* 122.128998 0.315000 5.378000 52.792999 0.000000* 122.128807 0.315004 5.377930 52.797356 0.000000* -0.000191 0.000004 -0.000070 0.004356 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s4) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.603850 -0.008892 -0.007315 -0.010037 -0.000486 76.509003 748.979004 3.179000 8959.433590 15060.677700 72.221550 748.912415 3.178767 8958.534180 15060.604500 -4.287453 -0.066589 -0.000232 -0.899414 -0.073242 p_XC(-,s5) results where '-' is in set 'SECT'.

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78 s1 s2 s3 s4 s5 -5.607608 -0.012873 -0.011296 -0.014018 -0.004466 0.000000* 849.520996 0.000000* 7559.000000 99637.156200 0.000000* 849.411621 0.000000* 7557.940430 99632.703100 0.000000* -0.109375 0.000000* -1.059570 -4.453125 p_XFL (LAB_FAC,SECT) Intermediate factor inputs p_XFL(-,s1) results where '-' is in set 'LAB_FAC'. labor 0.007427 60.900002 60.904526 0.004524 p_XFL(-,s2) results where '-' is in set 'LAB_FAC'. labor 0.006895 3541.367920 3541.612060 0.244141 p_XFL(-,s3) results where '-' is in set 'LAB_FAC'. labor 0.012259 15.223000 15.224866 0.001866 p_XFL(-,s4) results where '-' is in set 'LAB_FAC'. labor 0.003520 21113.478500 21114.220700 0.742188 p_XFL(-,s5) results where '-' is in set 'LAB_FAC'. labor -0.000461 215591.422000 215590.422000 -1.000000 p_XFKD (KD_FAC,SECT) Intermediate factor inputs p_XFKD(-,s1) results where '-' is in set 'KD_FAC'. capital -20.033243 10.638600 8.507343 -2.131257 p_XFKD(-,s2) results where '-' is in set 'KD_FAC'. capital -0.005517 1102.939940

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79 1102.879150 -0.060791 p_XFKD(-,s3) results where '-' is in set 'KD_FAC'. capital -0.000154 125.987000 125.986809 -0.000191 p_XFKD(-,s4) results where '-' is in set 'KD_FAC'. capital -0.008892 8250.000000 8249.266600 -0.733398 p_XFKD(-,s5) results where '-' is in set 'KD_FAC'. capital -0.012873 93277.500000 93265.492200 -12.007812 p_XEXP (SECT) Net Exports of commodities s1 s2 s3 s4 s5 -5.595454 0.000000* 0.001577 -0.001145 0.008407 61.000000 3969.547120 400.360992 20293.494100 78871.039100 57.586773 3969.547120 400.367310 20293.261700 78877.671900 -3.413227 0.000000* 0.006317 -0.232422 6.632812 Wage-fixed Scenario Output SETS ---No Name Size Description -------------------------------------1 SECT 5 Sectors 2 FAC 3 Factors 3 NUM_SECT 1 sector 1 4 TWO_SECT 4 sectors 2-5 5 NSEC_SECT 1 sector 2 6 FORE_SECT 1 sector 3 7 LAB_FAC 1 Labor Factor of Production, factor 1 8 KD_FAC 2 Capital and Land Factors, factors 2&3 9 LAND_FAC 1 Land Factor of Production, factor 3 VARIABLES --------No Name Size Arguments (if any) and Description ---------------------------------------------------------------1 p_Y 1 Total household expenditure 2 p_PC 5 (SECT) Price of commodities 3 p_PFL 1 (LAB_FAC) Price of Labor 4 p_PF 10 (KD_FAC,SECT) Price of factors 5 p_XCOM 5 (SECT) Total demand for (or supply of) commod ... 6 p_XFACL 1 (LAB_FAC) Total demand for (or supply of) factors

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80 7 p_XFACKD 2 (KD_FAC) Total demand for (or supply of) factors 8 p_XH 5 (SECT) Household consumption of commodities 9 p_XC 25 (SECT,SECT) Intermediate commodity inputs 10 p_XFL 5 (LAB_FAC,SECT) Intermediate factor inputs 11 p_XFKD 10 (KD_FAC,SECT) Intermediate factor inputs 12 p_XEXP 5 (SECT) Net Exports of commodities 13 p_PW 5 (SECT) World Price of Commodity 14 p_TLF 1 Total Labor Force 15 p_ULF 1 Unemployed Labor Force 16 p_T 1 Consumer Market Tax THE SHOCKS ARE AS FOLLOWS. p_PF 1 SHOCK = 25.000000 p_XFKD 2 SHOCK = -25.000000 END OF THE SHOCKS. THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE. p_Y Total household expenditure -0.264188 240336.562000 239701.625000 -634.937500 p_PC (SECT) Price of commodities s1 s2 s3 s4 s5 5.923539 0.000000* -0.000314 0.009806 0.000233 1.000000 1.000000 1.000000 1.000000 1.000000 1.059235 1.000000 0.999997 1.000098 1.000002 0.059235 0.000000* -0.000003 0.000098 0.000002 p_PF (KD_FAC,SECT) Price of factors p_PF(-,s1) results where '-' is in set 'KD_FAC'. land 33.143456 1.000000 1.331435 0.331435 p_PF(-,s2) results where '-' is in set 'KD_FAC'. land -0.082095 1.000000 0.999179 -0.000821 p_PF(-,s3) results where '-' is in set 'KD_FAC'. land -0.002291 1.000000 0.999977 -0.000023

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81 p_PF(-,s4) results where '-' is in set 'KD_FAC'. land -0.132315 1.000000 0.998677 -0.001323 p_PF(-,s5) results where '-' is in set 'KD_FAC'. land -0.191536 1.000000 0.998085 -0.001915 p_XCOM (SECT) Total demand for (or supply of) commodities s1 s2 s3 s4 s5 -5.663516 -0.082095 -0.001977 -0.142107 -0.191769 188.559998 7694.735350 405.838989 54213.156200 416915.562000 177.880875 7688.418460 405.830963 54136.117200 416116.062000 -10.679123 -6.316895 -0.008026 -77.039063 -799.500000 p_XFACL (LAB_FAC) Total demand for (or supply of) factors labor -0.184679 240322.391000 239878.562000 -443.828125 p_XFACKD (KD_FAC) Total demand for (or supply of) factors capital land -0.187670 -0.724580 102767.062000 842.587219 102574.203000 836.481995 -192.859375 -6.105225 p_XH (SECT) Household consumption of commodities s1 s2 s3 s4 s5 -5.842914 -0.264188 -0.263875 -0.273967 -0.264421 0.000000* 1126.350950 1.897000 17089.293000 222119.016000 0.000000* 1123.375240 1.891994 17042.474600 221531.688000 0.000000* -2.975708 -0.005006 -46.818359 -587.328125 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s1) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.663516 -0.074191 -0.073877 -0.083988 -0.074424 41.120998 7.355000 0.000000* 3.335000 43.889000 38.792103 7.349543 0.000000* 3.332199 43.856335 -2.328896 -0.005457 0.000000* -0.002801 -0.032665 p_XC(-,s2) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.670979 -0.082095 -0.081782 -0.091892 -0.082328 9.930000 870.853027 0.087000 303.221008 1131.000000

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82 9.366872 870.138123 0.086929 302.942383 1130.068850 -0.563128 -0.714905 -0.000071 -0.278625 -0.931152 p_XC(-,s3) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.595626 -0.002291 -0.001977 -0.012095 -0.002524 0.000000* 122.128998 0.315000 5.378000 52.792999 0.000000* 122.126198 0.314994 5.377349 52.791668 0.000000* -0.002800 -0.000006 -0.000650 -0.001331 p_XC (SECT,SECT) Intermediate commodity inputs p_XC(-,s4) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.718398 -0.132315 -0.132002 -0.142107 -0.132548 76.509003 748.979004 3.179000 8959.433590 15060.677700 72.133911 747.987976 3.174803 8946.701170 15040.714800 -4.375092 -0.991028 -0.004196 -12.732422 -19.962891 p_XC(-,s5) results where '-' is in set 'SECT'. s1 s2 s3 s4 s5 -5.774314 -0.191536 -0.191223 -0.201322 -0.191769 0.000000* 849.520996 0.000000* 7559.000000 99637.156200 0.000000* 847.893860 0.000000* 7543.782230 99446.085900 0.000000* -1.627136 0.000000* -15.217773 -191.070313 p_XFL (LAB_FAC,SECT) Intermediate factor inputs p_XFL(-,s1) results where '-' is in set 'LAB_FAC'. labor -0.074191 60.900002 60.854820 -0.045181 p_XFL(-,s2) results where '-' is in set 'LAB_FAC'. labor -0.082095 3541.367920 3538.460690 -2.907227 p_XFL(-,s3) results where '-' is in set 'LAB_FAC'. labor -0.002291 15.223000 15.222651 -0.000349 p_XFL(-,s4) results where '-' is in set 'LAB_FAC'. labor -0.132315 21113.478500 21085.543000 -27.935547 p_XFL(-,s5) results where '-' is in set 'LAB_FAC'.

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83 labor -0.191536 215591.422000 215178.484000 -412.937500 p_XFKD (KD_FAC,SECT) Intermediate factor inputs p_XFKD(-,s1) results where '-' is in set 'KD_FAC'. capital -20.088785 10.638600 8.501434 -2.137166 p_XFKD(-,s2) results where '-' is in set 'KD_FAC'. capital -0.082095 1102.939940 1102.034420 -0.905518 p_XFKD(-,s3) results where '-' is in set 'KD_FAC'. capital -0.002291 125.987000 125.984116 -0.002884 p_XFKD(-,s4) results where '-' is in set 'KD_FAC'. capital -0.132315 8250.000000 8239.083980 -10.916016 p_XFKD(-,s5) results where '-' is in set 'KD_FAC'. capital -0.191536 93277.500000 93098.843800 -178.656250 p_XEXP (SECT) Net Exports of commodities s1 s2 s3 s4 s5 -5.593462 0.000000* 0.000314 -0.009805 -0.000233 61.000000 3969.547120 400.360992 20293.494100 78871.039100 57.587986 3969.547120 400.362244 20291.503900 78870.851600 -3.412014 0.000000* 0.001251 -1.990234 -0.187500 p_ULF Unemployed Labor Force 3.574265 3.900000 4.039396 0.139396

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LIST OF REFERENCES Adelman, I., and S. Robinson. 1986. The Application of General Equilibrium Models to Analyze US Agriculture. Working Paper No. 423. University of California-Berkeley. Alavalapati, J., and W. Adamowicz. 1999. Modeling Economic and Environmental Impacts of Tourism in Resource Extraction Dependent Regions. Annals of Tourism 27:188-202. Alavalapati J., W. Adamowicz, and W. White. 1998. A Comparison of Economic Impact Assessment Methods: The Case Study of Forestry Developments in Alberta. Canadian Journal of Forest Resources 28:711-719. Albrecht, A., and S. Kanji. 2003. Carbon Sequestration in Tropical Agroforestry Systems. Agriculture, Ecosystems, and Environment 99:15-27. Baker, B. 1999. New National Plan to Control Pollution of Water by Livestock Waste. Bioscience 48:996. Bandara, A. 1991. Computable General Equilibrium Models for Development Policy Analysis in LDCs. J. Econ. Surv. 5:3-69. Bastian, C., D. McLeod, M. Germino, W. Reiners, and B. Blasko. 2002. Environmental Amenities and Agricultural Land Values: A Hedonic Model Using Geographic Information Systems Data. Ecol. Econ. 40:337-349. Blonigen, B., and W. Wilson. 1999. Explaining Armington: What Determines Substitutability Between Home and Foreign Goods? Can. J. Econ. 32:1-21. Cannell, M. 1999. Growing Trees to Sequester Carbon in the UK: Answers to Some Common Questions. Forestry 72:237-247. Cattaneo. A. 2001. Deforestation in the Brazilian Amazon: Comparing the Impacts of Macroeconomic Shocks, Land Tenure, and Technological Change. Land Economics 77:219-240. Deepak, M., C. West, and T. Spreen. Local Government Portfolios and Regional Growth: Some Dynamic CGE/Optimal Control Results. J. Regional Science 41:219-254. Dervis, K., J. de Melo, and S. Robinson. 1982. General Equilibrium Models for Development Policy. Cambridge University Press, New York, NY. 84

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85 Dixon, P., B. Parmenter, A. Powell, and P. Wilcoxen. 1999. Notes and Problems in Applied General Equilibrium Economics. Elsevier Science Publishers. 392p. Dixon, P., B Parmenter, and R. Rimmer. 1985. The Sensitivity of ORANI Projections of the Short-Run Effects of Increases in Protection to Variations in the Values Adopted for Export Demand Elasticities. pp 317-346 in K. Jungenfelt and D. Hague eds. Structural Adjustments in Developed Open Economies. Macmillan, London. Domingues, E., and E. Haddad. 2003. Analyzing the Spatial Impact of Tax Policies: An Interregional CGE Framework for Brazil. Working Paper. University of Sao Paulo, Brazil. Drake, L., K. Kumm, and M. Anderson. 1991. Does the Agriculture in Rottendalen Have Any Future? An Analysis of Landscape Values and Environmental Costs. Smaskriftsserien No. 48. Department of Economics, Swedish University of Agricultural Sciences, Uppsala. EPA 1972. Characteristics of Rainfall Runoff from a Beef Cattle Feedlot. Environmental Protection Technology Series. The United States Environmental Protection Agency. EPA-R2-72-061. EPA. 1995. Water Quality Functions of Riparian Forest Buffer Systems in the Chesapeake Bay Watershed. Technology Transfer Report prepared by the Nutrient Subcommittee of the Chesapeake Bay Program. United States Environmental Protection Agency. EPA 903-R.95-004. Ervin, E., and M. Dicks. 1988. Cropland Diversion for Conservation and Environmental Improvement: An Economic Welfare Analysis. Land Economics 64:256-268. FAS. 2004. World Beef Overview. United States Department of Agriculture Foreign Agricultural Service http://www.fas.usda.gov/dlp/circular/2004/04-03LP/beefoverview.html last accessed on 29 Oct. 2004. FDA. 2004. Commonly Asked Questions About BSE in Products Regulated by FDA's Center for Food Safety and Applied Nutrition. Unites States Food and Drug Administration. http://www.cfsan.fda.gov/~comm/bsefaq.html last accessed on 29 Oct. 2004. Garrett, H., W. Rietveld, and R. Fisher. 2000. North American Agroforestry: An Integrated Science and Practice. American Society of Agronomy, Madison, WI. Gerber, P., P.Chilonda, G. Franceshini, and H. Menzi. 2004. Geographical Determinants and Environmental Implications of Livestock Production Intensification in Asia. Bio. Tech. 96:263-276. Grierson, P., M. Adams, and P. Attiwill. 1992. Estimates of Carbon Storage in the Above-ground Biomass of Victorias Forests. Aus. J. Bot. 40:631-640.

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88 Swisher, M., J. Mullahey, W. DeBusk, M. Main, A. Shriar, G. Tanner, J. Selph, P. Hogue. and P. Bohlen. 2000. Ecological Economics of Floridas Ranches. University of Florida, Extensions, Gainesville, FL. Taylor, R. 1984. Beef Production and the Beef Industry: A Beef Producers Perspective. Burgess Publishing Co. Minneapolis, MN. 604p. Thissen, M. 1998. Two Decades of CGE Modeling: Lessons from Models for Egypt. SOM Research Report 99C02. University of Groningen, Groningen, Netherlands. USDA. 2002. Census of Agriculture: National, State, and County Tables. United States Department of Agriculture, Washington DC. 2002. USDA. 2003. The Economics of Food, Farming, Natural Resources, and Rural America. United States Department of Agriculture Economic Research Service. http://www.ers.usda.gov/news/BSECoverage.htm last accessed on 29 Oct. 2004. Wajsman, N. 1994. The Use of Computable General Equilibrium Models in Evaluating Environmental Policy. J. Environmental Management 44:127-143. Wang, Z. 1999. The Impact of Chinas WTO Entry on the World Labor-intensive Export Market: A Recursive Dynamic CGE Analysis. World Econ. 22:379-405. Weltz, M., G. Dunn, J. Reeder, and G. Frasier. 2003. Ecological Sustainability of Rangelands. Arid Land Resources and Management 17:369-388. West, G. 1995. Comparing Input-Output, Input-Output and Econometric, and Computable General Equilibrium Models at the Regional Level. Econ. Sys. Res. 7:209-238. Wu, X., E. Redeker, and T. Thurow. 2001. Vegetation and Water Yield Dynamics in an Edwards Plateau Watershed. J. Range Management 54:98-105. Xie, J.1996. Environmental Policy Analysis: A General Equilibrium Approach. Avebury Publishing Ltd. Aldershot, UK. 152p. Xie, J., and S. Saltzman. 2000. Environmental Policy Analysis: An Environmental Computable General-Equilibrium Approach for Developing Countries. J. Pol. Mod. 22:453-489. Zhang, X., and D. Xu. 2003. Potential Carbon Sequestration in Chinas Forests. Environ. Sci. Pol. 6:421-432.

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BIOGRAPHICAL SKETCH Troy Thomas Timko was born in 1975, in Lake Wales, Florida. He grew up in the small town of Sebring, Florida. After graduating from high school, he joined the United States Navy for a 6-year tour of duty as a nuclear reactor operator. Upon leaving the navy in 1999, he returned to college. In December 2002, he graduated with his bachelors degree from the Warrington College of Business Administration, at the University of Florida. He then continued his education at the School of Forest Resources and Conservation, at the University of Florida, in order to obtain his Master of Science degree. 89


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ECONOMIC IMPACT OF ADOPTING SILVOPASTURE IN FLORIDA:
A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS

















By

TROY THOMAS TIMKO


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE

UNIVERSITY OF FLORIDA


2004



































Copyright 2004

by

Troy Thomas Timko


































To my parents, Timothy and Patricia Timko; and to my brother and sister, Todd and
Tiffany Timko.















ACKNOWLEDGMENTS

I would like to acknowledge the School of Forest Resources and Conservation at

the University of Florida for supporting my education and research efforts. I would also

like to acknowledge funding support from the United States Department of Agriculture

through the Initiative for Future Agriculture and Food Systems.

Additionally, I would like to thank my committee members, Dr. Doug Carter and

Dr. Richard Kilmer for helping me to understand the economics necessary to conduct my

research; and for their guidance in the process of economic research and writing.

I give special thanks to my supervising committee chair, Dr. Janaki Alavalapati.

His expert knowledge and instruction were crucial in helping me gain the understanding

necessary to carry out this project. In addition, his ability to inspire confidence during the

many challenging phases of this research project helped me find the skills to overcome

the many hurdles that I faced, thereby making this research possible.

















TABLE OF CONTENTS

page

A C K N O W L E D G M E N T S ................................................................................................. iv

LIST OF TABLES ......... ............................ ........ ................... .. vii

LIST OF FIGU RE S ........................................ ........ ........................... .. viii

ABSTRACT .............. .......................................... ix

CHAPTER

1 IN TR OD U CTION ............................................... .. ......................... ..

B background ............................................................... .. ........ ...............
Problem Statem ent ........................................................ ...... .. ......... .. .. .2
Stu dy O bj ectiv es .................................................. ................ .. 2

2 RANCHING, RANCHING IMPACTS, AND SILVOPASTURE............................4

Cattle-ranching and the Cattle Industry .................................. ....................4
Environmental Impacts of Ranching ................... ..................... ...............5
Benefits A associated w ith Silvopasture ........................................ ...... ............... 7
Benefits Mitigating the Impacts of Cattle-ranching ...........................................7
A addition al B en efits ........ .............. ........................................ .......... .. .. ...9
Possible Externality D ilem m a ............................................................ .... ......... 9

3 LITERATURE REVIEW ........................................................... ....... ........11

Introdu action ..................................... .. ..... ........... ............. ..................11
Why Use Computable General Equilibrium Modeling? ............................................11
G general Equilibrium M odeling............................................................. ... ............ .11
Introduction ................................................................. ............................ 11
Input-O utput M odels ..................... .. ...... .................. .... .. .......... 12
Social A accounting M atrices ........................................ .......................... 13
Com putable General Equilibrium .................................... ........................ 14
Computable General Equilibrium Modeling ...........................................................17
Computable General Equilibrium Models for Policy Analysis.........................17
Computable General Equilibrium and the Environment............... ..................19


v









C o st-B en efit A n aly sis............................................................... .....................2 1
C o n trib u tio n ................................................................................................... 2 1

4 MODELING METHOD .............................................................................22

Introdu action .................................. ............ ................. ................. ..........22
Florida Computable General Equilibrium Model Data ...........................................22
M odel Structure ......... .... ...... .. .. .. ................ ............23
A additional equations ............................................................................. 26
M odel closures ........................................29
Modeling Shocks to the Existing Equilibrium .......................................... 30
Shock s M odeled .............................................................3 1
C o st-B en efit A n aly sis............................................................... .....................32

5 M O D EL R E SU L T S ......... ..... ................................................ ...................... 35

Simulation Results .......................... .............. ............. 35
W age-flexible Scenario ............................................................ .....................3 5
W age-rigid Scenario ............. ....................................................... ... .... ... ....4 1
C ost-B benefit A naly sis............ .... ......................................................... ...... .... ... .45

6 SUMMARY, IMPLICATIONS, AND RESEARCH OPPORTUNITIES ................49

S u m m a ry ........................ ..... .................. ................... ................ 4 9
Policy Im plications .................................... ..... .......... ......... .... 50
M odel Lim stations .................................... .. ......... ......... ... ..51
R research O pportunities...................................................................... ...................54

APPENDIX

A M O D E L O U T PU T ........................................................................... .................... 55

Wage-flexible Scenario Output ............................................................................55
W age-rigid Scenario O utput........................ .. ........... ..................... ............... 60

B ADJUSTED DATA SET MODEL OUTPUT ONE ...............................................65

Wage-flexible Scenario Output ............................................................................65
W age-fixed Scenario O utput ........................................................... .....................69

C ADJUSTED DATA SET MODEL OUTPUT TWO ...........................................75

Wage-flexible Scenario Output ............................................................................75
W age-fixed Scenario O utput ........................................................... .....................79
L IST O F R E FE R E N C E S ........................ .. ....... ................................... .................. ......84

B IO G R A PH IC A L SK E TCH ..................................................................... ..................89
















LIST OF TABLES


Table pge

4-1. Specification of the five-sector Florida CGE model ...............................................27

4 -2 E n dog en ou s v ariab les ............................ .................................................................2 8

4-3. Exogenous variables ............................................................. ............... 29

4 -4 C lo su res ............................................................................. 3 0

5-1. Macro-economic impacts of -25% land base and +25% capital costs .....................36

5-2. Commodity market impacts of -25% land base and +25% capital costs ...................37

5-3. Factor market impacts of -25% land base and +25% capital costs ..........................39

5-4. Macro-economic impacts of -25% land base and +25% capital costs .....................41

5-5. Commodity market impacts of -25% land base and +25% capital costs ...................42

5-6. Factor market impacts of -25% land base and +25% capital costs ..........................44

5-7. Estimated costs and benefits of providing silvopasture ...........................................46

5-8. Income adjusted changes in W TP estim ates.................................... ..................47

5-9. Income adjusted estimated costs and benefits of silvopasture .................................47

















LIST OF FIGURES


Figure


6-1. Difference in profitability for cattle-ranchers in Florida................ ................50


page















Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science

ECONOMIC IMPACT OF ADOPTING SILVOPASTURE IN FLORIDA:
A COMPUTABLE GENERAL EQUILIBRIUM ANALYSIS

By

Troy Thomas Timko

December 2004

Chair: Janaki R.R. Alavalapati
Major Department: School of Forest Resources and Conservation

Silvopasture, a type of ranching operation, combines trees with forage alongside

livestock and produces many environmental benefits over traditional ranching. These

benefits include carbon sequestration, biodiversity from wildlife habitat improvement,

and reduction in pollution runoff. However, policies targeted to further environmentally

benign practices often have far-reaching and sometimes unintended economic

consequences. It is therefore necessary to analyze the overall impacts of policies

influencing silvopasture to provide policy makers with information on how these policies

could affect the economy.

This research examines the effects of a 25% reduction available land base and a

25% increase in capital costs for cattle-ranchers in Florida. These shocks simulate the

adoption of silvopasture by Floridian cattle-ranchers. A computable general equilibrium

model was used to estimate the economic impacts of these policy shocks under wage-

flexible and wage-rigid closure scenarios. We examined changes in various demands for









commodities and factors of production for each of the five modeled sectors in response to

the 25% reduction available land base and the 25% increase in capital costs. We also

examined the impacts of policy shocks on macroeconomic variables (such as aggregate

household expenditure, wages and unemployment). In addition, a cost-benefit analysis

was conducted by comparing the costs to households in Florida and the benefits they

receive from the environmental services provided by silvopasture.

The model results showed that there would be a decrease in the economic welfare

of households in Florida under both closure scenarios after enforcing the policy shocks.

This decrease in welfare was more severe under the wage-rigid scenario in comparison to

the wage-flexible scenario. The cattle industry also experienced a moderate contraction

as a result of the policy shocks under both closure scenarios. The effects of the policies

on the welfare of Floridians changed slightly when the benefits that households perceived

for the services provided by silvopasture were included in the overall change in

household welfare. This change resulted in households in Florida experiencing a slight

increase in welfare under the wage-flexible scenario. However, under the wage rigid-

scenario, the model showed that Floridians still experienced a small decrease in overall

welfare.















CHAPTER 1
INTRODUCTION

Background

The cattle-ranching industry is an important agricultural enterprise in Florida, and

has a significant influence on the state's economy. In the past several years, public

concern for the environmental consequences of ranching has been increasing. One

concern is increase in water pollution due to runoff of nutrients such as phosphorus from

pastureland. This increase in phosphorus content causes eutrophication and subsequent

damage in the lakes into which the waters drain. In Lake Okeechobee, for example, the

phosphorus content has more than doubled, over the past century (Harvey and Havens

1999). Another concern is the production of methane by cattle. Methane is a greenhouse

gas and may contribute to global warming1 through the greenhouse effect.2 In addition,

the public is becoming more aware that there are opportunities available to help reducing

some of these impacts. Silvopasture, a form of agroforestry,3 offers environmental

services that can help meet the public's demand for mitigating the environmental impacts

of cattle-ranching. However, there will be costs associated with adopting silvopasture and





1 Global Warming is the potential augmentation of the greenhouse effect due to buildup of gasses that trap
heat in the atmosphere (Milich 1999).
2 The Greenhouse Effect is the sum of interactions between the heat that is attempting to escape from the
earth to space and the molecules of various gasses that trap this heat, reradiating it within the atmosphere,
and impeding its loss to space (Milich 1999).

3 Agroforestry is the management of land for the simultaneous production of food, crops, and trees.









with providing these services to the public. Our study provides policy makers with more

information on what these costs would be, if ranchers in Florida adopt silvopasture.

Problem Statement

Previous research suggests that households in Florida prefer to have the above

mentioned environmental services associated with the adoption of silvopasture (chapter

2) by ranchers. However, most of the environmental benefits provided by silvopasture are

external to ranchers. In contrast, ranchers directly experience the costs of altering their

ranching operations. Before making policy decisions regarding silvopasture, policy

makers need information on the economy-wide impacts that these policy changes would

have on Florida. Our study aimed to fill that gap by investigating how policies requiring

Floridian cattle ranchers to adopt silvopasture would impact the Florida economy. We

also aimed to analyze how the well-being of households in Florida will be impacted by

these policies.

Study Objectives

Our primary objectives were to analyze economy-wide impacts of a policy

requiring all cattle ranchers in Florida to adopt silvopasture practices. Policies that

required ranchers to adopt silvopasture would result in a decrease in the land available for

cattle production by ranchers. They would simultaneously cause the operating costs of

ranchers to increase. This study simulates the effects of both of these shocks being

enforced on the cattle-ranching sector. This task was addressed through a computable

general equilibrium analysis of the economy of Florida. The next two research questions

were addressed.

* Question 1: How will the modeled changes impact the overall economy of
Florida?









Question 2: How will the welfare of Floridians change as a result of the simulated
shocks when the environmental benefits of the policies are taken into account?

These two questions were explored under wage-flexible and wage-rigid scenarios.














CHAPTER 2
RANCHING, RANCHING IMPACTS, AND SILVOPASTURE

Cattle-ranching and the Cattle Industry

Cattle-ranching in the North America began several hundred years ago, at the time

of colonization of the New World. It provided an innovative land-use strategy that

facilitated the settlement of the frontier, many times at the expense of native peoples

(Jordan 1993). Since the time of colonization, cattle-ranching has managed to

successfully spread throughout the United States. Over the last few decades the area of

land being used for livestock production has been growing. In 1984, nearly 1 billion of

the approximately 2.2 billion acres in the United States were grazed by livestock (Taylor

1984). Today, rangelands and pastures are found in all 50 states, and some studies

estimate that they account for 55% of the country's land surface area (Weltz et al. 2003).

The cattle or beef industry is a major component of the agriculture industry in the

United States. In 2003, the retail equivalent value of the United States cattle and beef

industry was estimated at $70 billion dollars and the value of calf and cattle production

was estimated at $33.2 billion (USDA 2003). The large difference between the two

estimated values is due to the great deal of segmentation in the U.S. beef industry. The

beef industry includes various loosely interlocking segments such as seed-stock

producers, commercial cow calf producers, stocker operators, feeders, packers and

retailers (Taylor 1984). However, our study was primarily concerned with the portion of

the cattle industry that includes the raising of cattle on rangelands and pasturelands.









The United States as a whole is a net importer, and is the largest importer of beef

from the world market. Before the disruption of the beef trade due to discovery of BSE4

in the U.S., Canada was the largest source of imported beef for the United States, and

Japan was the largest purchaser of U.S. beef exports. With nearly 100 million cattle and

calves, the U.S. has the forth-highest cattle population behind India, Brazil and China.

(FAS 2004). The Florida cattle-ranching industry, which contributes more than $300

million to the Florida economy annually, is a major agricultural enterprise and has a

significant influence on the state's economy. According to USDA census data for Florida

for 2002 (USDA 2002), there are approximately 1.74 million cattle in the state on over

19,000 ranches, making Florida the tenth largest cattle producing state in the U.S. Due to

the large size and nature of this industry, it can have significant impacts on environmental

quality.

Environmental Impacts of Ranching

The scale of the cattle industry in the United States makes it difficult for it to

operate without impacting the environment to some extent. There are two main ways that

the cattle industry adversely affects the environment. First, water pollution problems can

result when water in the form of rainfall runoff comes into contact with manure and

carries high concentrations of solids, nutrients, and disease organisms into surface waters

and ground waters. Polluted runoff is the major contaminant of U.S. waterways according

to the Environmental Protection Agency (EPA 1972). Water quality surveys conducted in

twenty-two states found that out of 694,000 miles of river, 35,000 miles were adversely

affected by animal feeding operations. Nitrogen and Phosphorus are both nutrients often

4 BSE or Bovine Spongiform Encephalopathy (more commonly known as "mad cow disease") is a fatal
neurodegenerative disease in cattle which may be transmittable to humans (FDA 2004).









associated with accelerated eutrophication.of surface water. Algae blooms ofPfiesteria

piscidida and Cytosporidium in drinking water may be associated runoff from animal

waste (Baker 1999). Phosphorus management strategies are often identified as important

in limiting surface water eutrophication from agricultural sources since blue-green algae

are able to utilize atmospheric nitrogen, thus leaving phosphorus as the limiting

developmental factor for blue-green algae. (Gerber et al. 2004)

Environmental degradation from cattle-ranching is not, however, limited to water

pollution. Global climatic change in the form of global warming can be attributed to

several sources. The production of carbon dioxide through burning of fossil fuels and

other sources, the production of methane, the release of nitrous oxide primarily from the

application of fertilizer, and the production of ozone are major sources of the greenhouse

gasses that influence global warming (Milich 1999) Cattle-ranching contributes to global

warming through the greenhouse effect via the production of the greenhouse gas,

methane. Methane is the second most significant greenhouse gas and is expected to

contribute to 18% of the global warming from now until the year 2050. The largest

source of methane emissions, 30%, is enteric fermentation from livestock, followed

closely by methane emissions from rice paddies at 25 %. Also, due to the combination of

factors such as their great numbers, large size, and high energy intake; cattle produce

70% of global methane produced by animals, humans included (Milich 1999). The

quantity of methane released to the atmosphere is much less than the quantity of carbon

dioxide, however, methane is twenty times as effective in trapping heat on a per molecule

basis (Harrington and Lu 2002).









Regardless of the environmental impacts associated with cattle production, the

worldwide consumption of beef is not likely to decrease dramatically in the foreseeable

future. It is, therefore, necessary for society to seek solutions to help mitigate the

environmental impacts of ranching while allowing producers to continue to provide the

goods that people desire. The adoption of silvopasture practices by ranchers has been

suggested as a possible means of helping to mitigate these environmental impacts.

Benefits Associated with Silvopasture

Silvopasture is a form of agroforestry that combines spatial and rotational growth

of timber, forage, and livestock, has many associated environmental benefits (Husak and

Grado 2000). Silvopasture may be able to mitigate of some of the negative impacts of

cattle production while, in addition, providing other environmental services to the public.

There are many benefits associated with silvopasture, which fall into several

categories such as water quality improvement, soil conservation, carbon sequestration5

and improvement of wildlife habitat (Shrestha and Alavalapati 2004). In a recent study,

Shrestha and Alavalapati (2004) estimated the public's willingness to pay for these

environmental services. Their research suggests that households in the Lake Okeechobee

Watershed in Florida would be willing to pay $30.24 $71.17 per year for five years to

receive these environmental benefits.

Benefits Mitigating the Impacts of Cattle-ranching

The adoption of silvopasture practices by ranchers would help to mitigate the

negative impact that cattle-ranching has on water quality. Growing trees on farms and

ranchlands would improve the quality of water through the reduction of pollution runoff,


5 Carbon sequestration is the removal of carbon dioxide from the atmosphere and its storage in the form of
biomass in the terrestrial biosphere. (Albrecht and Kandji 2002)









the replenishment of ground water aquifers, and the maintenance of the long-term water

cycle. (Wu et al. 2001, Stednick 1996) Many silvopasture arrangements include tree and

grass buffer strips as part of their overall design. Research suggests that tree and grass

buffer strips twenty to thirty meters in width control up to 77% of phosphorus and 80% of

nitrogen runoff (EPA 1995; Gerrett et al. 2000). Reduction in the quantities and stocking

rates of cattle supported by silvopasture cattle ranches as opposed to conventional

ranches would also have the effect of mitigating pollution by the reduction of the quantity

and the concentration of animal wastes as the number and density of animals is reduced.

Adoption of silvopasture would also help to mitigate the negative effects that

cattle-ranching has on the atmosphere through carbon sequestration (Shrestha and

Alavalapati 2004). Carbon sequestration has been shown to be a cost effective means for

mitigating global climatic change by compensating6 for greenhouse gas emissions

(Albrecht and Kandji 2002, Zhang and Xu 2003). The quantities of carbon dioxide stored

as a result of adding tree cover can be substantial. According to recent literature, an acre

of southern pine grown in silvopasture on a twenty year rotation could absorb anywhere

between 145 to 220 tons of carbon dioxide (Cannell 1999, Grierson et al. 1992). As with

the reduction in water pollution, reduction in the quantities and stocking rates of cattle

supported because of adoption of silvopasture on cattle ranches would also cause a

reduction in greenhouse gas emissions locally. This portion of the mitigating effect of

silvopasture adoption might be reduced to some extent, however, if imports to the region

increase signifying increased production in foreign regions.


6 The sequestration of carbon would not directly reduce the amount of methane in the atmosphere, however,
since both are greenhouse gasses, reduction of atmospheric carbon can help to offset methane emissions in
the global warming context.









Additional Benefits

Throughout Florida, private pasture and ranchlands play an integral role in

providing habitats for a diverse selection of wildlife species. Some of the species

inhabiting these areas include the white-tailed deer, the Sandhill crane, and the

Burrowing owl (Morrison and Humphrey 2001, Swisher et al. 2000). Many of the

species that the trees and vegetation on these lands provide habitats for are threatened or

endangered. Additionally, studies have been conducted in other states suggesting that

agricultural lands that include wildlife habitat command higher prices per acre than

similar land dominated by agricultural production (Bastian et al. 2002). This increased

value may be attributed to the increased opportunities for hunting and wildlife watching

on the land. Silvopasture may also be more aesthetically pleasing than open pastures

while the additional tree cover could provide livestock with increased protection from

summer heat and winter chill (Nowak et al. 2002)

Possible Externality Dilemma

The above reasons provide explanation for the increase in public interest in the

incorporation of silvopasture technology on ranchlands as a means for realizing

environmental services that they prefer to have. However, the adoption of silvopasture

may result in a positive externality7 problem for ranchers. In general, the environmental

services or benefits that would be provided through silvopasture technologies on

ranchlands are external to the production decisions of ranchers. Ranchers would incur

increased management costs as well as reduced cattle output as a result of adopting



7 Posi i\ c externalities exist when the marginal social benefit of production and or consumption exceeds
the marginal private benefit i.e. production and/or consumption generate external benefits that may go
under-valued by the market" (Jones 2004).









silvopasture on their lands (Shrestha and Alavalapati 2004). Some examples of these

increased costs include: establishment costs associated with tree planting, purchase of

fencing, and temporary withdrawal of livestock from the areas.

While cattle ranchers' costs would be increasing, the benefits of their actions would

be enjoyed by the public. There is no direct market mechanism for the public to pay the

ranchers in compensation for these increased costs. However, cattle ranchers may be able

to pass on some or all of these increased costs to consumers through raising their prices

and not be worse off.

This provides some rationale for the government to seek information regarding the

economic impacts of policies requiring ranchers in Florida to adopt silvopasture

practices. If research shows that the cattle-ranching industry is severely negatively

impacted by being required to adopt silvopasture practices, then policies that would serve

to internalize the benefits that ranchers would provide to the public through adoption of

silvopasture might be justified. Such policies might include additional taxes on

households that the government could transfer to the cattle ranchers in exchange for

provision of the environmental services the public desires.














CHAPTER 3
LITERATURE REVIEW

Introduction

In this chapter a review of review of general equilibrium modeling techniques is

provided. Next, the structure of general equilibrium models is discussed, followed by a

review of literature on applications of CGE models. Finally a review of some examples

of how cost benefit analysis has been applied to economics and the environment is

provided.

Why Use Computable General Equilibrium Modeling?

Policy makers require information concerning the probable effects of implementing

policies that would require ranchers in Florida to adopt silvopasture practices since such

policies could drastically influence Florida's economy. Analysts often utilize partial

equilibrium analysis to determine the possible effects on an industry as a result of policy

actions. While partial equilibrium analyses provide highly detailed information on the

likely effects of policies to one particular industry, their downside is that they neglect

intersectoral interactions within the economy. In order to address the economy-wide

impacts of policies in a more comprehensive manner, general equilibrium modeling

techniques have been developed and applied to policy analysis.

General Equilibrium Modeling

Introduction

General Equilibrium modeling techniques include several modeling methods, each

of which is employed by policy analysts and researchers to conduct economy-wide









impact analyses. As the techniques progress from the relatively simple input-output

models to the increasingly more complex social accounting and computable general

equilibrium models, the data requirements for the methods increase greatly. Likewise, the

capacity of the modeling techniques to capture economy-wide impacts increases as the

models become more general. The availability of data, the time and funding available for

the analysis and the specific characteristics of the situation to be modeled are considered

in selecting an appropriate model.

Input-Output Models

Input-output (I-O) models are the simplest of the three types of regional modeling

systems presented. The essence of I-O models is that industries are related to each other

through transactions between them through the buying and selling of raw materials

(Pyatt, 1999). Of the general equilibrium modeling techniques discussed, I-O models

have the benefit of having the lowest data requirement. There are several characteristics

of I-O models that can, however, limit their ability to adequately analyze regional

impacts of policies (West, 1995; Miller and Blair 1985).

The first of these characteristics is that input-output models have infinitely elastic

supplies of inputs into production processes, resulting in a lack of supply-side constraints.

Therefore, I-O models offer no market feedback mechanism between primary factors and

final commodity demands (West 1995). The failure of this model type to show these

connections is of concern because while sectors may not be linked directly by commodity

flows in the economy, they still might interact through competition for scarce resources,

most notably competition for primary factors of production (West 1995). Therefore, I-O

models may only be appropriate for use in modeling situations where primary factors of









production are less than fully employed and where producing sectors have excess

capacity (Patriquin 2000).

Another limiting characteristic of I-O models is that prices of inputs and outputs

are fixed therefore preventing the model from being able to capture the behavioral

adjustments of consumers and producers that would occur in the face of endogenous

prices. This limitation implies that I-O models may be appropriate only for the extremely

short-run (Patriquin 2000). Another assumption of I-O models that restricts their

application to the extreme short-run is that in input-output models, production

technologies are assumed to have fixed input proportions therefore preventing

substitution between factors of production.

Alternative modeling methods have been designed because of the inherent

limitations of I-O models. Nevertheless, I-O models still serve a useful purpose as

descriptive tools of economies. Additionally, they often serve as the base for other more

complex modeling procedures (West 1995).

Social Accounting Matrices

The social accounting matrix (SAM) is the next more complex method presented

for modeling regional economic policy impacts. Similar to I-O models, SAM models

represent inter-industry linkages in the economy. In both I-O and SAM frameworks, the

purchase of an intermediate input by one sector represents the sale of that same input by

another sector. However, because the SAM utilizes double-entry bookkeeping, each

transaction appears in the accounts of two different sectors, rather than in a single cell as

represented in I-O (Robinson et al. 1999).

Additionally, in the SAM model framework, income for each sector must be equal

to that sector's total expenditures. Total expenditures for an industry could include costs









such as intermediate inputs, wages, imports, as well as capital services. Incomes appear

along the rows of social accounting matrices and expenditures down their columns. The

budget constraint, therefore, requires that the row and column sums must be equal

(Robinson et al. 1999).

Similar to I-O models, SAM models can be utilized to model economic impacts.

However, they have the added benefit of being able to capture distributional impacts as

well. For example, households could be disaggregated within the SAM framework based

on household income levels therefore giving them the ability to describe which segment

of households would win or lose based upon a given policy shock (Stone 1985, Patriquin

2000).

SAM models are more complex and provide a greater level of sophistication in

their ability to capture more detailed aspects of economic changes than IO models.

However, because SAM and IO models are based on similar assumptions, SAM models

are still vulnerable to some of the inherent limitations of IO models. Neither SAM nor IO

models account for supply constraints or the substitution between inputs (Adelman and

Robinson 1986). Additionally, in SAM models, the technical coefficients remain fixed as

do prices. Because these limitations can reduce the applicability of both IO and SAM

models, considerable effort has been devoted to deriving, developing and applying

computable general equilibrium models.



Computable General Equilibrium

Computable general equilibrium (CGE) models represent then next more

complicated building block in modeling that will be presented. CGE models incorporate

a set of behavioral equations that describe the economic behavior of the agents identified









in the model as well as the technological and institutional constraints that they face

(Thissen 1998). These sets of equations are responsible for the enhanced flexibility and

applicability of CGE models. The incorporation of these behavioral equations makes

CGE models more robust than their predecessors because these models are able to

capture certain economic behavioral relationships and characteristics that the neither IO

nor SAM was capable of integrating into its framework. One such advantage of CGE

models is their ability to handle endogenous prices therefore permitting the prices of

inputs to vary with respect to changes in output prices. This feature allows the responses

of CGE models to economic shocks to more closely approximate the responses of agents

found in the economy.

Another advantage of CGE models is that they can include constraints on the

availability of primary inputs. This feature of CGE models is significant because of the

dampening effects that this type intersectoral linkage, based on resource limitations, can

provide. Whereas in IO and SAM models, expansions in one industry lead directly to

expansion in other industries based on the technical coefficients linking the industries in

the models. These dampening effects can be seen when an increased quantity of a limited

factor of production such labor, for example, is needed for an expanding industry. This

industry's increased demand for labor will limit the supply of that factor available for

other industries, resulting in a certain degree of contraction8 in other industries competing

for that factor (Patriquin 2000).





8 The actual degree of contraction for industries competing for the same primary factor as is an expanding
industry will be dependent on several factors including, but not limited to: the production structures of
those industries, factor intensities, and the technical coefficients linking models industries to each other.









CGE models also have the distinct advantage of being highly customizable. For

example, a CGE model could be constructed with the assumption that production

structures allow no substitution between intermediate inputs and primary factors, similar

to the structure of I-O models. In CGE models, assumptions such as these can also be

relaxed or modified to a desired level depending on the specific characteristics of the

economy being analyzed by the model (Alavalapati et al. 1998).


Although CGE modeling techniques offer several advantages over both I-O and

SAM, they are not without weaknesses and limitations. One weakness of CGE models is

that many CGE models are deterministic models. In other words, for a given data set and

specified shock, a CGE model will determine one set of outputs without allowing for

uncertainty within its framework (Xie 1996). Also, Shoven and Whalley (1984) pointed

out that no consensus exists regarding the determination of the values of elasticities and

other key parameters, yet they often play a pivotal role in the specification of CGE

models. Another disadvantage of CGE models is that they frequently require large

amounts of data. Often, to reduce the demand for data for CGE models, the models are

calibrated on a benchmark data set for a single year. While making the models quicker

and easier to construct, this can result in the models being very sensitive to one year data

(Xie 1996). In addition, earlier CGE models were either static or only quasi-dynamic in

nature and treat inter-temporal behavior such as investment inadequately. CGE models

have also been criticized for being too complicated to understand for decision makers and

the general public.

As research has moved forward on the construction and application of CGE

models, many of their limitations and weaknesses have been addressed. Investment









behavior has received more rigorous treatment in recent CGE models (Xie 1996).

Cattaneo (2001), for example, combines factors such as agent's marginal propensity to

save, investment demands, and planning horizons to develop a robust dynamic

macroeconomic model of the effects of deforestation in the Brazilian Amazon. Also,

econometric approaches have been more widely adopted for the estimation of parameters

utilized in their behavioral equations (Patriquin 2000). In the following sections, a brief

description of CGE modeling history and application is provided.

Computable General Equilibrium Modeling

Computable General Equilibrium Models for Policy Analysis

Over the past several years, CGE modeling has become an increasingly popular

tool among researchers for policy analysis. They have been used to address a multitude

of policy issues including: choice of development strategies, trade policies, income

distribution, long term growth and structural changes, and structural adjustments to

external shocks (Bandara 1991). In addition, much effort has been spent recently to

develop CGE models that can simultaneously capture the above economic policy

analyses while also describing the effects of the modeled policy shocks on the status of

the environment.

Much of basis for current CGE modeling techniques originated several decades ago

with the work of a small group of economists. One of the most notable is Lief Johansen,

whose multi-sector growth model was the first empirically based price endogenous model

analyzing resource allocation issues Although the model was originally developed as a

forecasting tool, Johansen applied the model to answer policy questions in Norway

(Shoven and Whalley 1984). Another important figure in the inception of CGE

development was Arnold Herberger. In his 1962 article, "The Incidence of the









Corporation Income Tax" Herberger was the first economist to investigate taxes

numerically in a two-sector general equilibrium framework (Shoven and Whalley 1984).

Equally important was Ronald W. Jones' paper, "The Structure of Simple General

Equilibrium Models." In this paper, he provides a detailed analysis of the structure of the

simple competitive model of production, highlighting the similarities existing among

several problems in comparative statics and economic growth (Jones 1965). This paper

proved to be influential in the developmental course of CGE modeling.

As the number of CGE models began to increase, so too did the range of policy

issues addressed by modelers. CGE models designed to analyze various impacts of

changes in trade policies emerged. These trade policy impact analysis models frequently

belonged to one of two major categories. The first major category was comprised of

single-country models designed to investigate how developments abroad affected

individual economies. The second major category contained multi-country models,

which were designed to tackle global trade issues. (Shoven and Whalley 1984) Both of

these two different types of trade models often incorporated the Armington9 assumption,

which differentiates home and foreign goods as imperfect substitutes in consumption.

(Blonigen and Wilson 1999) Examples of CGE models utilized to analyze trade policies

include: Dervis et al. (1982), Dixon et al. (1985), Mercenier and Waelbroeck (1985), and

Shoven and Whalley (1984).

Models have also become more complex in their ability to capture inter-temporal

features within their structures. Through the inclusion of equations describing how an


9 Armington Assumption The assumption that internationally traded products are differentiated by country
of origin. This assumption is now standard in international CGE models, and is used to generate smaller
and more realistic responses of trade to price changes than implied by homogeneous products.( Deardorff,
2001)









economy evolves, inter-temporal CGE models can be used to describe the manner in

which an economy reaches its equilibrium. This is a desirable feature to incorporate in

CGE models because policy makers are often concerned with the rate at which an

economy will move towards the long run equilibrium point as well as other transitional

characteristics (Dixon et al 1999). However, inter-temporal models have the downside of

being more complex to construct and harder to solve, and thus are more costly.

Nevertheless, many dynamic models have now been constructed. These models can

increase the meaningfulness of welfare change calculations in comparison to static

modeling because of the abundance of dynamic elements in the real world (Seung and

Kraybill 1999). Examples of inter-temporal, or dynamic, CGE models can be found in:

Keuschnigg and Kohler (1994), Wang (1999), and Deepak et al. (2001).

Computable General Equilibrium and the Environment

Policy makers' decisions regarding environmental legislation implementation is

often reliant on whether or not the legislation will harm economic growth. Various

interest groups constantly influence policy makers to obtain the outcome in their favor.

On one side are industry representatives, who typically forecast increased unemployment,

reduction in international competitiveness, and depression of economic growth.

Environmental lobbyists, conversely, stress the negative consequences of factors such as

pollution while they downplay trade-offs between economic growth and a clean

environment (Waj sman 1994). In effort to supply policy makers with unbiased

information, scientists are required to carefully examine issues and describe both the

probable positive and negative consequences of proposed environmental legislation.

A wide range of environmental-economic issues have been examined by CGE

analyses. These environmental CGE models, although varying greatly in regional size









and functional specification, typically fit into three general categories. The first category

is comprised simply of standard CGE models that have been applied to address

environmental issues. For example, in Olatubi and Hughes (2002), they use a general

equilibrium model of Louisiana to analyze the effects of the Wetland Reserve Programlo

on the state's economy.

The second category includes environmentally extended CGE models. In order to

provide more detailed descriptions of the environmental impacts of economic policy

shocks, these models usually give indications of changes in pollution emissions using

fixed coefficients per unit of sectoral output. In this type of model, these indicator

outputs do not feed back into the behavioral equations of the CGE model, and therefore

do not change the behavioral specifications of the models (Xie and Saltzman 2000).

Models such as Patriquin's (2000) environmentally extended CGE model of the Foothills

Model Forest in Alberta, Canada belong to this category.

Models that introduce environmental feedback to the economic systems belong to

the last major category of environmental CGE models. Jorgenson and Wilcoxen (1990)

specify pollution control costs in their production functions in order to achieve this

environmental and economic integration. Alavalapati and Adamowicz (1999) utilize a

simple general equilibrium model to study the interactions among tourism, other

economic sectors, and the environment. In their tourism impact model, they specify the

damage to the environment as a function of output and the extent of land used for

production (Alavalapati and Adamowicz, 1999).


10 Wetland Reserve Program (WRP) -"The Food, Agricultural, Conservation, and Trade Act of 1990
(FACTA) amended the Food Security Act of 1985 (FSA) to provide for the establishment of the WRP. The
goals of the program are to ensure 'no net loss' of remaining wetlands and to increase the quality and the
quantity of the nation's wetlands." (Olutabi and Hughes, 2002)









Cost-Benefit Analysis

The interaction of the economy and the environment are not modeled endogenously

in this research. However, we utilize willingness to pay results for the environmental

services of silvopasture of the residents in the Lake Okeechobee watershed and compare

these values to changes in the income of Floridian households in a cost-benefit analysis

framework.

Cost benefit analysis is a decision making tool used frequently by economists and

other decision makers to determine whether or not a project is worth the necessary

investment. This tool has been applied to many economic and environmental issues.

Ervin and Dicks (1988) utilized a cost benefit analysis of the Conservation Reserve

Program to analyze the economic welfare consequences of converting cropland to

alternative uses to enhance conservation and environmental goals. Similarly, Moss et al.

(1996) use a cost benefit approach to compare the social, financial, and ecological costs

of several different land use options.

Contribution

The main contribution of this work will be estimating probable economy-wide

impacts of policies requiring that all Floridian cattle ranchers adopt silvopasture. These

impacts will be determined through the application of a five sector CGE model of the

state. Additionally, this work will give some indication of possible changes in the

welfare of Floridian households by comparing the perceived benefits of the services

silvopasture provides with the costs to households.














CHAPTER 4
MODELING METHOD

Introduction

This chapter outlines the construction of the model used in this research. First,

information on the data source and aggregation is given. Next, the structure of the general

equilibrium model used in the analysis is presented, including the equations modeled, the

closures chosen, and the shocks implemented. Finally, a discussion of the cost benefit

approach used to determine the change in the well being perceived by households in

Florida under the two chosen closures is given.

Florida Computable General Equilibrium Model Data

The data utilized in the construction of the social accounting matrix for this model

was obtained from the IMPLAN database of the Minnesota IMPLAN Group. The

original 1999 database for Florida consists of 528 individual sectors or industries.

Industries were aggregated into five sectors for the final SAM based on the goals of this

research and the general industry product categories. The five aggregated sectors are

cattle, other agriculture and resources, forestry, manufacturing, and services.

Estimates indicate that the approximate the size of the cattle sector in Florida is

approximately $300 million annually (Stainback et al. 2004). In aggregating sectors of

the IMPLAN database, we determined that the size of the cattle sector in this model is

approximately $188 million annually. This difference is most likely attributed to our

selection of which industries to include in the cattle sector aggregation. For the model in

this study, only range and ranch fed cattle are included in the cattle sector. Other value









adding cattle related sectors, such as the various meat processing industries, are instead

treated as part of the manufacturing sector.

Model Structure

The computable general equilibrium model that has been constructed in this study

is a customized version of a Stylized Johansen Model. The development of the theoretical

structure of a Johansen model includes formulating several sets of equations. Included in

these are equations for: household and final commodity demands, intermediate and

primary factor inputs, commodity pricing, and market clearing (Dixon et al. 1999). These

equation sets form the framework for the model and determine how the model will react

in response to shocks applied to the system of equations. Following the general structure

from Dixon et al. (1999), a customized version of the Stylized Johansen Model is

developed below utilizing the percentage change form equations necessary for solution of

the computable general equilibrium model.

Xio = Pi for i= 1, ..,5 (3-1)

Equation 3-1 represents the household demand equation for the commodities in the

model. It shows that the household demand (xio) for a commodity will increase as the

income increases, or the price of that commodity decreases. Each sector in the model

produces one commodity, therefore resulting in five household demand equations, one

for each commodity.

f6 2
Xii =X p i aPt + Z as P (3-2)
t=1 f=1

fori = 1, .,8,j = 1, ., 5









Equation 3-2 represents the input demands for each of the inputs for industries one

through five. Equation 3-2 says that the change in the demands for inputs by a sector is a

function of several variables. First, it shows that the demands for the inputs to a sector

(xi) will increase as the demands for the sector's output commodity (xj) increase. It also

shows that the change in the demand for input from a given source will be inversely

related to the change in the relative price of that input.

For this research, the factors of production were disaggregated from labor and

capital only, to labor capital and land. In the Stylized Johansen model, both labor and

capital are treated the same, with one model price each for both wages and capital rental

rates, thus allowing both labor and capital to be mobile between the various producing

sectors. In this research, however, we chose to model capital (and the disaggregated land

factor) as sector specific, while allowing labor to remain mobile. This is accomplished by

utilizing sector specific prices (p,) in equation 3-2 for the capital and land factors of

production inputs for the sectors.


P, = P K+ ~ JPJ) forj = 1,..., 5 (3-3)
t=1 f=1



Equation 3-3 is the percentage change form of the zero pure profits condition for

the model. This equation shows that the percentage change in the price of good j will be

a weighted sum of the percentage changes in the input prices for that industry's

intermediate inputs and primary factors. The weights, or alphas, are the proportions of

the cost that each input comprises in the total cost of all inputs for that commodity. The

RHS of equation 3-3 is identical to the bracketed portion [*] of Equation 3-2.









6
x, = XAj fori = ,.., 5 (3-4)
J=0

Equation 3-4, the market clearing equation for the commodities of the CGE model,

equates a weighted average of the percentage changes in the various demands for each

commodity to the percentage change supply of that commodity. The weights, or betas,

are the proportions of the demand that each source of demand (j) comprises in the total

demand for each i good. For the purposes of this research, an additional demand, net

export demand, was added to the model. Therefore, the sources of demand for a

commodity in the model are: household demand (j = 0), intermediate demands from the

five industries (j = 1,...,5), and net export demand for the commodity (j = 6 ). The

addition of export demand causes the model to depart from the Stylized Johansen model

by transforming the CGE model from a closed economy model to an open economy

model.

5
xf = xfj,8f forf= 1,2, and 3 (3-5)
J=1

Equation 3-5, the market clearing equation for the primary factors in the CGE

model, equates a weighted average of the percentage changes in the demands for each

factor to the percentage change in the quantity supplied of that factor. The factors in the

model are labor (1), capital (2), and land (3).

p2 = 0 (3-6)

Equation 3-6 represents the selection of sector two as the numeraire, or the unit of

measure for money in the model. The other prices in the model are given in terms of the

price of the numeraire commodity.









Additional equations

For the purposes of this research, additional equations we added to the model in

order to incorporate features not captured in a Stylized Johansen model. A new export

demand equation was added in order to change the model from a closed economy to an

open economy. In addition, an equation for labor employment was added, to allow the

model to capture changes in the labor employment levels.

xie =-P, + Ap for i= 1,..., 5 (3-7)

Equation 3-7 represents the percentage change form of the net export equation for

the CGE model. It shows that export demand for a good (xe) is inversely related to the

change in the price of that good (p,) resulting in a negative export elasticity of demand.

More specifically, we have assumed, for simplicity of model construction, that the export

elasticity of demand is negative one. Additionally, we assume that the economy of

Florida is small relative to the rest of the world. Hence, we have chosen the world prices

for the commodities (p,,, as exogenous in the model.

S= a,cu +cae (3-8)

Equation 3-8 relates a weighted sum of the change in unemployed labor force and

employed labor force to changes in the total employable labor force available. The

addition of this equation gives the model the capability of capturing the effects of the

policy shocks on the level of unemployment in the economy when the change in the

employed labor force is chosen as endogenous.









Table 4-1. Specification of the five-sector Florida CGE model
3-1 Xo y i = 1,...,5

3-2 6 2 V i= 1,...,8
x,= 7- opt + a p= 5
[ t=1 f f=1 '

3-3 6 2 j = 1,...,5

t=1 f=1

3-4 6 i = 1....,5
Xi XY 18Y
j=0

3-5 5 f = 1,2,3
x1 YZXfj/3fi
j=1

3-6 P2 =

3-7 i i = 1,...,5


3-8 = au+ae


The set of equations in the CGE model form a matrix. For this matrix to have a

solution, the number of equations in the model must be equal to the number of

endogenous variables. By nature of their construction, these models will have more

variables than the number of equations. As a result, some of the variables must be

selected as exogenous, and the rest retained endogenous in order to fulfill the

mathematical requirements for finding a solution to the model. Below is a list of the

variables that we have chosen to retain endogenous in the model.









Table 4-2. Endogenous variables
y Household income

Pi i=1,...,5 Commodity price

Pf f=2; j=1,...,5 Price of land

xi i=1,...5 Demand for commodities

Xf f=2 Demand for capital

Xf f=3 Demand for land

xio i=1,...,5 Household consumption of commodities

ij i=1,...,5; j=1,...,5 Intermediate commodity inputs

xg f=l;j=1,...,5 Labor factor input demand

xg f=2,3; j=1,...,5 Capital and land factor input demands

xie i=1,...,5 Net export demand for commodities

u Unemployed labor force

Pt t = 6 Price of labor ( wage rate)

Xf f=l Total demand for labor


The rest of the variables, which are listed in Table 4-3 are selected as exogenous.

Two of these variables, the aggregate supply of labor, and the wage rate, are utilized to

create the two different closures that we utilize in the analyses in this research. Only one

of the two will be exogenous at a given time. For the flexible wage scenario, the

aggregate supply of labor will be selected as exogenous and the wage rate will be

retained endogenous. For the rigid wage scenario, the wage rate will be selected as









exogenous and the aggregate supply of labor will become endogenous. This will allow

changes in unemployment to occur in the second model closure.

Table 4-3. Exogenous variables
Piw World price of commodity

1 Employable labor force in the economy

Pf f=2; j=1,...,5 Price of capital

xf f=3; j=1,...,5 Supply of land to industries

Xf f=l Total demand for labor

Pt t = 6 Price of labor (Wage rate)


Model closures

The way that equilibrium is ensured in a CGE model is known as the closure of the

model and is determined by selecting the set of variables that will be exogenous to the

system. The chosen closure has significant theoretical implications directly affecting the

behavioral characteristics of the model. Each of the two main schools of closure has its

merits. Acknowledging that there are significant differences in the ways the model will

react depending on the closure selected, we have chosen to model the shocks under two

different main closure scenarios. This treatment should provide policy makers with more

information on how the proposed policy shocks could influence the economy. The first

closure we have chosen to enforce on the model has a variable wage rate, with the

aggregate labor demand fixed as exogenous. This closure forces wage rate to vary such

that full employment is ensured. This selection is similar to the Johansen11 closure and


1 "In the Neoclassical closure, aggregate investment is determined by aggregate savings, which in turn are
determined endogenously through the fixed savings rate out of after tax income and the government deficit.
In Johansen closure, aggregate investment is assumed to be fixed exogenously and the savings rate is









falls within the main school of Neoclassical closures. In the second closure, which is

more structuralist in nature, labor employment is endogenous and the fixed the wage rate

and forces the level of employment to vary to achieve equilibrium in the model. Table 4-

4, derived from the model closure discussion in Kraev (2003), highlights some of the

differences between these two main schools of closure for CGE modeling.

Table 4-4. Closures
Neoclassical Structuralist
Elasticity Micro Macro
Full Yes Yes Not necessarily Not necessarily
employment (can restrict (emphasize
prices and labor macroeconomic
mobility) disequilibria)
Full capacity Yes Yes Not necessarily Not necessarily
utilization (neoclassical (disequilibrium
disequilibrium) possible)
Marginal Yes Yes Not necessarily Not necessarily
productivity
determines
prices
Substitution Perfect Limited Varies (limited Varies (limited
elasticities substitutes substitution or perfect or perfect
(Armington substitution) substitution)
assumption)
Characteristics that separate some of the different closure types are given with a list of
the main closure categories across the top. The only characteristic that separates a purely
neoclassical closure from an elasticity-structuralist model is the utilization of limited
substitution elasticities (Kraev 2003).

Modeling Shocks to the Existing Equilibrium

The construction of the computable general equilibrium model establishes a base,

static, or equilibrium condition of the economy. This condition is described by the

calibrated data in the SAM database that is used as an input for the CGE model. The

equation sets explained above create the structure of the economy being modeled and

assumed to generate the required savings. Johansen explicitly argues that macroeconomic fiscal and
monetary policies, presumably outside the CGE framework will ensure that savings are generated to
balance the investment."(Robinson 2003)









describe how that economy will react in the event of a shock to the economic system.

Once the model is in place, many simulations may be performed depending on the

specific shock, or shocks, that the modeler has chosen to enforce on the model. Below,

we describe the rationale for the shocks that we modeled.

Shocks Modeled

Ranchers would have to modify the composition of their ranchlands in the course

of changing from traditional ranching operations to silvopasture. Planting additional trees

on these lands will reduce the land area available to cattle ranchers for production of their

livestock. In order to model the effects of the ranchers implementing this operational

change, we chose as an exogenous variable the land factor of production for sector one,

which represents the quantity of land available to the cattle industry for production. We

then impose a twenty-five percent reduction in the cattle sector's available land base by

applying a shock of -25% to the supply of the land factor of production for sector.

Recently, research has been conducted on the values of trees or forests on ranchlands.

That research was modeled such that for silvopasture adoption by ranchers, 20% of land

would be taken out of production from ranching with additional lands taken out for the

creation of riparian buffer strips (Shrestha and Alavalapati 2004). The level of

environmental improvement offered by this size of land use change is similar to the level

of improvement on which the willingness to pay data that was utilized in this study was

also based. For that reason, a value of 25% was chosen for the negative shock to the land

base for ranchers to include the change in land available due to adding trees to the

ranchlands as well as to account for additional land for riparian buffers.

In addition, the adoption of silvopasture will cause ranchers to expend more in

capital costs on items such as tractor and other timber management equipment rental









required to practice silvopasture. The actual increase in capital costs for ranchers'

adoption of silvopasture could vary greatly depending on factors such as the size of the

ranching operations, the method chosen to protect young trees from cattle, and the

amount of the necessary equipment already owned by the rancher. Because of the great

deal of variation possible in cost increases, a twenty- five percent increase in capital costs

was chosen in order to ensure this portion of the total shock would be significant in

comparison to the shock to the land base. This is simulated in the model by applying a

25% increase to the cost of capital for the cattle sector. The effects of each shock are

analyzed under each of the two closure scenarios wage-flexible, which ensures no change

in employment, and wage-rigid, which allows for changes in employment.

Cost-Benefit Analysis

This analysis is conducted in order to give policy makers more information on the

effects on the welfare of Floridians due to policies on silvopasture. In order to conduct a

cost benefit analysis, both the costs and the benefits have to be expressed in the same

units. The most common unit for both to be expressed in is in currency or dollar values.

We utilized the change in the income of the households in Florida for each scenario as the

cost side of the cost benefit analysis. The determination of the benefits side of the

analysis is slightly more complicated, however. Because there is no direct market for the

environmental services of silvopasture, alternative means of valuing these benefits must

be utilized in order to compare their benefits to the costs in monetary terms. In order to

address this problem, we utilize a study that was conducted to estimate the public's

willingness to pay for these environmental services. Shrestha and Alavalapati (2004)

determined that, within the Lake Okeechobee watershed, the public's willingness to pay

for the environmental services associated with silvopasture totaled $924.4million.









There are 1.34 million households in the Lake Okeechobee watershed and 6.34

million households in the entire state of Florida (Shrestha and Alavalapati 2004).

However, without having more information relating to the intensity of preferences for

these benefits statewide as compared to the intensity of preferences in the Lake

Okeechobee watershed, an accurate extrapolation cannot be calculated. Therefore, we

have chosen to utilize the WTP estimate for the Lake Okeechobee watershed as a

conservative estimate of the total willingness to pay of all Florida households.

Another complication in using this WTP value directly is that this willingness to

pay estimate is the value that these households attach to having these benefits forever.

The WTP value must therefore be converted to a yearly amount in order to compare it

with the results from the CGE analysis, which are based on yearly data from 1999.

PV = pmt/i

The yearly contribution necessary to realize this benefit forever can be calculated

by utilizing the above equation for a perpetual annuity. We chose a common discount rate

of .05, or 5% to calculate the present value. We acknowledge that with higher estimates

of the discount rate the estimate yearly benefit will increase.

Additionally, if environmental services provided by silvopasture are not inferior

goods, then the public's desire for the environmental services of silvopasture will

decrease as household income decreases. Income elasticities of demand for

environmental services similar to those provided by silvopasture have been estimated in

Sweden. Hokby and Soderqvist (2001) estimated that the income elasticity of demand for

reducing nitrogen loads in waters in Sweden were about 1.10. Also, the income elasticity

of demand for preserving agricultural landscape in Sweden was estimated at 0.91 by






34


Drake et al. (1991). We assume that the elasticitiy of demand for the environmental

services provided by silvopasture are comparable and we will therefore use an income

elasticity of demand of 1.0 to evaluate the change in demand for these environmental

services as income changes.














CHAPTER 5
MODEL RESULTS

Simulation Results

The customized Five Sector Florida CGE model described in chapter three is

utilized to simulate the impacts of cattle ranchers in Florida adopting silvopasture

practices on their ranch and pasturelands. The simulations were conducted under two

closure scenarios. Under the first closure, the wage rate was endogenous to the model and

flexible. Under the second closure scenario, however, the wage rate is held constant, or

rigid, by making the wage rate exogenous and applying no change to the price of labor.

Wage-flexible Scenario

Two simultaneous shocks, a 25% decrease in the land base available for cattle

production and a 25% increase in capital costs for the ranching sector, are imposed on the

CGE model for each of the two closure scenarios. The 25% decrease in land base

available for production of cattle represents land that will be taken out of cattle

production and instead be utilized for growing trees. The increase in capital costs in

sector one represents additional capital costs, such as tractor and other timber

management equipment rental required to practice silvopasture. The shocks simulate the

effects that adopting silvopasture will have on the cattle-ranching sector directly. The

model then simulates, through the CGE framework, how the changes imposed on the

cattle sector will affect the rest of the economy of Florida. The results of the wage-

flexible scenario are presented in Tables 5-1, 5-2, and 5-3.









Table 5-1. Macro-economic impacts of -25% land base and +25% capital costs
Variable % Change Original level New level Change
Total household expenditure(millions) -0.009218 $240,336.56 $240,314.41 -$22.15
Wage rate -0.006444 1.00 0.99993556 -0.00006
Percent unemployment 0 3.9 3.9 0

Table 5-1 presents some of the macroeconomic impacts of the shocks on the

Florida economy. Household demand for goods has dropped, -$22.15million, reflecting

the negative effect on the income of Floridians as a result of these environmentally

benign policies. Although this is a large change relative to other magnitudes in this

simulation, it reflects a drop of just under one one-hundredth of a percent of the total

expenditures of Floridians. The wage rate drops only slightly, -0.0006%, to keep

employment levels constant at the 1999 level for Florida of 3.9%.













Table 5-2. Commodity market im acts of -25% land base and +25% capital costs
Sector % Change Original level ($) New level ($) Change ($)
Price of commodity 1 3.03419 1.00 1.03 0.030
2 0.00000 1.00 1.00 0.000
3 -0.00082 1.00 1.00 0.000
4 0.00060 1.00 1.00 0.000
5 -0.00436 1.00 1.00 0.000

Total commodity demand 1 -2.94759 188.56 183.00 -5.558
(levels in millions) 2 -0.00286 7,694.74 7,694.52 -0.220
3 0.00074 405.84 405.84 0.003
4 -0.00521 54,213.16 54,210.33 -2.825
5 -0.00232 416,915.56 416,905.89 -9.668

HH commodity demand 1 -2.9540 0.00 0.00 0.000
(levels in millions) 2 -0.0092 1,126.35 1,126.25 -0.104
3 -0.0084 1.90 1.90 0.000
4 -0.0098 17,089.29 17,087.62 -1.677
5 -0.0049 222,119.02 222,108.23 -10.782

Export demand 1 -2.94507 61.00 59.20 -1.796
(levels in millions) 2 0.00000 3,969.55 3,969.55 0.000
3 0.00081 400.36 400.36 0.003
4 -0.00059 20,293.49 20,293.37 -0.121
5 0.00436 78,871.04 78,874.48 3.443

Table 5-2 presents the economic impacts to the markets for commodity outputs of

sectors one through five for a 25% decrease in land base and 25% increase in capital

costs for cattle ranchers in Florida under the flexible wage rate scenario. Changes to

commodity prices, total commodity demands, total household demands, and export

demands are shown. In addition, the pre-shock levels, post-shock levels, and level deltas

are given. The commodity output results show that the price of sector one's output

(commodity one) has increased by 3.03%. This increase in the output price of the cattle

sector can be attributed to the increases in their input costs that are passed along to









consumers12 through raising the price of their product. The price of commodity two

remains unchanged since it has been fixed as the numeraire. The manufacturing sector,

sector four, also experiences a small increase of 0.0006% in the price of its output. One

reason for this increase is because sector four contains many of the cattle consuming

industries, such as meat packing plants as well as sausage and other beef processing

industries. The price of their cattle input goes up, so they must adjust their output price as

well to maintain zero pure profits.

The shocks to the cattle sector have caused the overall economy of Florida to

contract. As a result of this contraction, consumer demand for most of the sector outputs

has declined. This drop in demand has the largest impact, in terms of dollar value

decrease, to the service sector, sector five, which experiences a drop of $9.67 million.

The service sector is the largest sector in the model however, and this drop reflects a

change of only -0.0023%. The shocks were applied to the cattle sector directly, thus this

sector experienced the largest percentage drop of -2.95% in demand following their

relatively high price increase.

As a result of the contracting economy, Floridian households have less income to

spend on consumption of goods. Hence the demand for all commodities by households

has decreased accordingly. Although the model shows that largest decrease in household

demand by percentage is in sector one, households do not actually directly consume

output from the cattle sector. Households instead purchase the processed cattle output

from the manufacturing sector. This output carries along with it a higher price due to the


12 Consumers, in this case, refers not only to households in the model, but to all who purchase the output of
sector one, including: sector one through purchase of its own output as an intermediate input, other sectors
through purchase of intermediate inputs from sector one, and foreign importers purchase of commodity
one.










increase in intermediate costs of the input from sector one. This price increase along with

the decrease in household expenditure causes the manufacturing sector to experience the

second largest drop in consumer demand of the five industries, a decrease of -.0098%.

Export demand changes are the least complicated changes to analyze with this

model since we assume a constant exchange rate and the changes in export demand are

therefore functions of only the change world price of the commodity and the change in

the price of that sector's commodity. Because our treatment of Florida follows the small

country assumption, changes in the production of goods in the Florida economy have no

effect on world prices. We have therefore fixed world prices exogenously and export

demand changes remain functions only of changes in the goods' prices. Accordingly,

there was a rise in net exports for commodities three and five and a decline in net exports

for commodities one and four. The demand for net exports for commodity two remains

fixed because of the selection of sector two as the numeraire in the model.

Table 5-3. Factor market impacts of -25% land base and +25% capital costs
Variable Sector % Change Original level ($) New level ($) Change ($)
Labor Demand 1 0.00386 75.11100 75.11 0.00290
(levels in millions) 2 0.00358 3541.36792 3,541.49 0.12675
3 0.00636 15.22300 15.22 0.00097
4 0.00183 21113.47800 21,113.86 0.38574
5 -0.00024 215591.42200 215,590.90 -0.51742

Capital Demand 1 -20.03132 10.63860 8.50755 -2.13105
(levels in millions) 2 -0.00287 1102.93994 1102.90834 -0.03160
3 -0.00008 125.98700 125.98690 -0.00010
4 -0.00462 8250.00000 8249.61910 -0.38090
5 -0.00668 93277.50000 93271.26626 -6.23374

Land Prices 1 33.32833 1.00000 1.33328 0.33328
2 -0.00287 1.00000 0.99997 -0.00003
3 -0.00008 1.00000 1.00000 0.00000
4 -0.00462 1.00000 0.99995 -0.00005
5 -0.00668 1.00000 0.99993 -0.00007









Table 5-3 presents the impacts to Florida's factor markets as a result of the shocks

simulating the adoption of silvopasture by Florida's ranching sector. Since this scenario

is under flexible wage rate assumption, aggregate demand for labor is fixed exogenously

and the price of labor varies to maintain full employment of labor in Florida. Although

the aggregate supply of labor is fixed in this closure, labor is not sector specific. This

allows unrestricted mobility of labor within the economy. Each sector has its own degree

of labor intensity. Thus, as demand for output from each of the sectors changes each

sector will shift its demand for labor by the amount necessary, relative to its labor

intensity, to maintain the desired level of output. This can be observed as the individual

sectors adjust their employment levels as a result of the shocks. Sector five, which has a

relatively large decrease demand for output, $9.67million, experiences in a decrease in its

demand for labor even with the decrease in the wage rate. Labor from this sector then

mobilizes and relocates to the other sectors, keeping the aggregate labor supply constant.

Capital is sector specific in this model and therefore cannot move between sectors.

The decrease in the output demand for sector one combined with the higher costs of

capital in that sector, have resulted in a large drop in capital demand in sector one. This

decrease in demand by sector one does not benefit the other sectors because of the

immobility of capital. Therefore, the other sectors do not experience a gain in resources

available that might be felt under a mobile capital model specification. The other four

sectors each experience a slight reduction in capital utilization as a result of the

contracting economy. Supply of land for all sectors was held exogenous in the model,

but the land rental rates were allowed to vary. Land, like capital, is treated as sector

specific, and sectors one (cattle-ranching), two (other agriculture) and three (forestry) are









the land utilizing sectors of this model. As a result of the reduction to the land available

for production of cattle for sector one, the rental rates for ranchlands have increased

dramatically, 33.24%. The remaining four sectors each experience a slight decrease in

rental rates.

Wage-rigid Scenario

Following the same reasoning for simulating the changes to the economy as a result

of Florida's cattle ranchers adopting silvopasture as in the flexible wage scenario,

identical shocks, consisting of a 25% decrease in the land base available for cattle

production and a 25% increase in capital costs for the cattle-ranching sector, are imposed

on the CGE model for the wage-rigid scenario. The results of the wage-rigid scenario are

presented in Tables 5-4, 5-5, and 5-6.

Table 5-4. Macro-economic impacts of -25% land base and +25% capital costs
Variable % Change Original level New level Change
Total household expenditure(millions) -0.137373 $240,336.56 $240,006.40 -$330.158
Wage rate 0 1.00 1.00 0.000
Percent unemployment 1.842082 3.9 3.9718412 0.072

Some of the key macroeconomic impacts of the shocks on the Florida economy are

presented in Table 5-4. This closure represents a more short-term reaction of the

economy to the imposed shocks. This is because, in the event of a shock to an economy,

the initial response to changes in demand for labor will be met by changes in the

unemployment rate rather than by changes in the real wage. (Domingues and Haddad

2003) The unemployment rate in Florida increases by from 3.90% to 3.97% as a result of

imposing the shocks on the economy and the wage rate remained fixed exogenously by

the closure. According to the U.S Census Bureau, there were 7,407,458 people employed

in Florida in 1999. According to these employment levels, 5322 Floridians will lose their

jobs as a result of the policy shocks when the unemployment level in Florida rise from










3.90% to 3.97. As a result of this increase in unemployment and the contraction

experienced throughout the Florida economy, households have much less income to

spend and therefore total household expenditures decreased by over $330million. It is

clear from these macro-economic responses that under this closure, the imposed shocks

will have a more negative effect on the economy.

Table 5-5. Commodity market impacts of -25% land base and +25% capital costs
Variable Sector % Change Original level ($) New level ($) Change ($)
Price of commodity 1 3.03727 1.00 1.03 0.030
2 0.00000 1.00 1.00 0.000
3 -0.00016 1.00 1.00 0.000
4 0.00510 1.00 1.00 0.000
5 0.00012 1.00 1.00 0.000

Total commodity
demand 1 -2.98542 188.56 182.93 -5.629
(levels in millions) 2 -0.04269 7,694.74 7,691.45 -3.285
3 -0.00103 405.84 405.83 -0.004
4 -0.07390 54,213.16 54,173.10 -40.061
5 -0.09972 416,915.56 416,499.83 -415.732

HH commodity
demand 1 -3.0813 0.00 0.00 0.000
(levels in millions) 2 -0.1374 1,126.35 1,124.80 -1.547
3 -0.1372 1.90 1.89 -0.003
4 -0.1425 17,089.29 17,064.95 -24.346
5 -0.1375 222,119.02 221,813.62 -305.400

Export demand 1 -2.94797 61.00 59.20 -1.798
(levels in millions) 2 0.00000 3,969.55 3,969.55 0.000
3 0.00016 400.36 400.36 0.001
4 -0.00510 20,293.49 20,292.46 -1.034
5 -0.00012 78,871.04 78,870.94 -0.095

The economic impacts to the markets for commodity outputs of sectors one through

five for a 25% decrease in land base and 25% increase in capital costs for cattle ranchers

in Florida under the wage-rigid scenario are presented in Table 4-5. The price change in

the cattle sector under this closure was approximately the same as the change in the

previous, wage-flexible, closure. This is due to the changes imposed on the cattle sector









being much larger than other general equilibrium effects. In general the other commodity

price changes are slightly higher in magnitude as apposed to the first closure, with the

exception of the numeraire, whose price remains fixed.

The demand for output from the cattle sector remains nearly the same as in the first

closure because the price change for sector one is relatively large and therefore enforces a

more binding constraint on the amount of commodity one purchased by the other sectors

than changes in their input demands would impose. The remaining four sectors

experience a large decrease in total demand. The largest portion of this decrease in total

demand comes from the decrease in demand by households for those commodities.

Household demand drops sharply because the impact on employment under the rigid

wage scenario causes a large decrease in the amount of money households have to spend.

As in the first closure, change in the net export demand is dependent only on the change

in the commodity price.












Table 5-6. Factor market impacts of -25% land base and +25% capital costs
Variable Sector % Change Original level ($) New level ($) Change ($)
Labor Demand 1 -0.03858 75.11100 75.08 -0.02898
(levels in millions) 2 -0.04269 3541.36792 3,539.86 -1.51174
3 -0.00119 15.22300 15.22 -0.00018
4 -0.06880 21113.47800 21,098.95 -14.52628
5 -0.09960 215591.42200 215,376.70 -214.71828

Capital Demand 1 -20.06023 10.63860 8.50447 -2.13413
(levels in millions) 2 -0.04269 1102.93994 1102.46912 -0.47082
3 -0.00119 125.98700 125.98550 -0.00150
4 -0.06880 8250.00000 8244.32392 -5.67608
5 -0.09960 93277.50000 93184.60027 -92.89973

Land Prices 1 33.19060 1.00000 1.33191 0.33191
2 -0.04269 1.00000 0.99957 -0.00043
3 -0.00119 1.00000 0.99999 -0.00001
4 -0.06880 1.00000 0.99931 -0.00069
5 -0.09960 1.00000 0.99900 -0.00100

The impacts under the wage-rigid scenario to Florida's factor markets as a result of

the shocks simulating the adoption of silvopasture by Florida's ranching sector are

presented in Table 4-6. Since wages are fixed exogenously under this closure, industries

are forced to reduce their employment levels in order to reduce total spending on wages

to meet the new, lower desired levels of output while minimizing costs. The largest

impacts are in the service and manufacturing sectors, which reduce spending on labor by

$214.7 million and $14.5 million respectively.

As in the previous closure, capital is sector specific. Therefore capital cannot move

between the model's five sectors. In sector one, the combined effects of the decrease in

the output demand for sector one and the higher costs of capital in that sector have

resulted in a large drop in capital demand in sector one. As a result of the contracting

economy, the service sector's capital expenditures decreased by $92.8 million. The









manufacturing sector also experienced a large reduction in capital expenditures of

$5.7million.

Due to the reduction to the land available for production of cattle for sector one, the

rental rates for ranchlands have increased dramatically, 33.19%. Because land is sector

specific as well, an increase in the price of ranchland has no direct effect on the prices of

other land in the model. Therefore, the decrease in output levels and therefore the desire

for land input causes the land rental rates to drop since land quantities are exogenous.

The model produced a large number of outputs for changes in the intermediate

demands by sectors. These changes in the demands for each sector's intermediate good

input are not presented here. However, the full set of output data for each closure

scenario is available in Appendix A.

Cost-Benefit Analysis

The introduction of these shocks simulated the effect on the economy of cattle

ranchers across the state of Florida adopting silvopasture. Impacts to the economy of

Florida varied greatly, depending on the closure chosen, with a decrease of household

income of $22.16 million for the wage-flexible closure and $330.16 million under the

wage-rigid closure. This reduction in income, however, may be partially compensated

for by benefits gained by Florida residents in the form of environmental services resulting

from the adoption of silvopasture practices.

As explained in chapter 3, there is no direct market for the environmental services

of silvopasture; therefore, an alternative means of valuing these benefits must be utilized

in order to compare their benefits in monetary terms. We utilized the WTP estimate of

$924.4 from the Lake Okeechobee watershed from Shrestha and Alavalapati (2004) as a

conservative estimate of the statewide WTP of Floridians for these services. When









converting this value to a yearly benefit contribution, we calculated an annual benefit

value of $46.22million. Utilizing the costs from the decrease in the income of

households and the above calculated benefit value we estimated the total net benefit

below.

Table 5-7. Estimated costs and benefits of providing silvopasture
Wage-flexible Wage-rigid
Watershed WTP estimate (Benefit) 46.22 46.22
Income change (Cost) -22.16 -330.16
24.06 -283.94

Table 5-7 compares willingness to pay for the environmental services of

silvopasture with the costs in terms of the income change that households were subjected

to under each of the two model closures. This comparison shows that the benefits

outweigh the costs by $24.06 million for wage-flexible scenario, but that the costs exceed

the benefits by $284 million under the wage-rigid scenario.

We use an income elasticity of demand of 1.0 to evaluate the change in demand for

the environmental services provided by silvopasture. The change in income of Floridians

was observed to be -0.0092% and -0.1374% for the wage-flexible and wage-rigid

closures respectively. Since we utilize the WTP estimates for the environmental services

of silvopasture in place of the demand for this non-market good, the estimates need to be

adjusted for the change in income experienced by households in Florida. Although

household incomes in Florida were decreased by millions of dollars under both closures,

this amount is actually a very small percentage of overall household income and

therefore, the adjusted WTP estimates differ very little form the previous estimates. The

equations for the change in willingness to pay for the wage-flexible and wage-rigid

scenarios are presented next.









aWTPf 8WTP 8WTP 8WTP
WTPex and, WTPngd nWTPngd
aie 0.000092 n' B d -0.001374

Assuming the income elasticity as 1.0, these equations yield.

7WTP(ex = (-0.000092)(1.0) and, 8WTP,d = (-0.001374)(1.0)

OWTPl = -0.000092 and, 8WTP, = -0.001374

By multiplying these deltas by the original WTP and subtracting the output from

the original estimates yields the income adjusted values in table 4-8.

Table 5-8. Income adjusted changes in WTP estimates
Wage-flexible Wage-rigid
Income adjusted WTP estimate $46.2157 $46.1565
Base WTP estimate $46.2200 $46.2200
Change -$0.0043 $0.0635

We arrive at Table 5-9 by substituting these new values into the cost-benefit

analysis.

Table 5-9. Income adjusted estimated costs and benefits of silvopasture
Wage-flexible Wage-rigid
WTP estimate (Benefit) 46.216 46.156
Income change (Cost) -22.160 -330.160
24.056 -284.004

These results show that for the wage-flexible closure scenario, the benefits

outweigh the costs by $24.056million. However, for the wage-rigid closure scenario, the

costs exceed the benefits by $284.004million. These results present evidence that

silvopasture will not necessarily give a positive benefit to society under all closure

scenarios. This leads to some important questions. If the wage-rigid scenario is a more

short term of the interactions in the economy, how long is the duration of these more

intense negative impacts? How long will it take before industries make adjustments to

their wage rates? Questions such as these could possibly be answered by constructing






48


more advanced CGE models. Although these results do not currently provide conclusive

evidence that the overall benefits to households will exceed the costs, it does show the

possibility that they could. The above findings provide grounds for the need for more

research in this area with more complex and detailed CGE models capable of adequately

handling these issues.














CHAPTER 6
SUMMARY, IMPLICATIONS, AND RESEARCH OPPORTUNITIES

Summary

In our study, we used a five sector CGE model of Florida to analyze the impacts to

the economy of Florida in response to shocks simulating the adoption of silvopasture by

all cattle ranchers in the state. We wanted to answer two questions. Primarily, we wanted

to know how the modeled policy changes would impact Florida's economy.

We analyzed this question under both a flexible and fixed wage enclosure and found that

the incomes of Floridians would decrease by $22.16 million for the wage-flexible closure

and $330.16 million under the wage-rigid closure. In addition, under the fixed wage

enclosure scenario, we estimated that 5,322 Floridians would lose their employment. The

cattle-ranching sector is found to lose approximately 3.0% or $5.6million as a result of

the shocks. This decrease in sector activity is small when compared to the magnitude of

the imposed shocks on that sector since ranchers pass on the higher costs of business to

the manufacturing sector, which eventually results in higher beef prices for consumers.

We also wanted to answer the question of how the welfare of Floridians would

change as a result of the policy shocks when the environmental benefits of the policies

are taken into account. Utilizing a cost benefit approach, we found that under the wage-

flexible closure, households in Florida would come out ahead under the flexible wage

scenario by $24.056million. However, under the wage fixed scenario, they would be

worse off by $284.004million.









Policy Implications

The two different scenarios paint different pictures for policy makers of the

possible severity of the impacts having ranchers in Florida adopt silvopasture. Since

scenarios modeled with rigid wages reflect a shorter term than flexible wage scenarios,

the actual response by the economy might be that first the economy reaches an

equilibrium more closely in line with the wage-rigid scenario, and then over time moves

towards the flexible wage scenario. This might imply that over the long run, the public

will be better off in response to Floridian ranchers having to adopt silvopasture.

However, policy makers are understandably reluctant to enact policies that will cause a

large number of their constituent voters to lose their employment. This might imply to

policy makers that they should employ policies that would not cause the entire state to

adopt silvopasture at once.


S2


P i







Qi Q0 Q

Figure 6-1. Difference in profitability for cattle-ranchers in Florida









Although smaller incremental policies that required only certain areas to adopt

silvopasture would certainly decrease the total economic impact to Florida, they would

have much different effects on the local cattle ranchers. This can be shown from Figure

6-1. If policy makers implemented policies that affected all of the state's cattle ranchers,

then price will increase from PO to P1 as quantity dropped as a result of the higher cost of

producing the cattle under silvopasture. If only a small percentage of ranchers were

affected at a time however, they would act as price takers, and the price may not rise.

This would reduce the profits of the ranchers practicing silvopasture as they would no

longer be able to capture the pink shaded area as part of their revenues and would only

receive the blue shaded area representing revenues from the reduced quantity at the

original price. This might imply that policy makers will have to develop policies such as

tax incentives or carbon sequestration payments that would compensate the ranchers who

are forced to adopt silvopasture for the environmental services they produce since they

would be unable to pass their increased costs on to consumers through higher cattle

prices.

Model Limitations

Because of the static nature of our model, it does not show the reactions of the

economy in response to the shocks over time. Information on how the economy behaves

in the transition period from the initial shock to the equilibrium point would be useful to

policy makers. Another limitation is that we employed relatively simplistic Cobb-

Douglas utility and production functions in the model. Other functional forms might

improve how accurately the model reflects the behavior of economic agents in the real

world. Changes such as these also greatly increase the data requirements of CGE models.

Also, this model is constructed with data aggregated into only five sectors. With a greater









degree of sector disaggregation, the number of possible policy shocks that this model

could capture would increase. Also, the impacts of adopting silvopasture in Florida could

be more accurately modeled. Also, we did not include economic returns from timber

sales as part of the economic gain to ranchers because the returns would be received far

in the future relative to the model's yearly data and including a present value of these

gains might present a distorted picture of the benefits ranchers actually observe.

In addition to these limitations, the model results are highly affected by the

allocations of costs described by the database that we used. The model is particularly

sensitive to changes in the costs of the factors of production to which the shocks to the

model were applied. It is possible that the proportion of factor costs associated with labor

for the cattle sector in our model were overestimated while the proportion of factor costs

associated with land rental costs were underestimated. In order to show how the results

of the model could vary with a change in these factor cost proportions, we utilized an

finished cow-calf budget, provided by Dr. Anton in the Food and Resource Economics

Department at the Univeristy of Florida, to estimate the costs in labor for the cattle

industry at $24 million annually. We then adjusted the original database values

accordingly and ran the model again for both the wage-flexible and wage-rigid scenarios.

The results for the model after these changes were made are provided in

Appendix B. Under the wage-flexible scenario, this change caused the impact to

Floridian household income to increase to $96 million from the $22 million estimated

loss using the original model data. This larger drop can be attributed to the increased

impact of the shock to the land base in the cattle sector as the proportion of that lands cost

contribution increases. Similarly, the impact to the cattle sector increased as well from a









$5.5 million decrease in industry demand to a $22.9 million drop after changing the

model data.

The increase in the impacts of the shocks as a result of changing the database

input data were felt much more severely under the wage-rigid scenario. Under this

scenario, the impacts on household income increased from a $330 million decrease to a

drop of over $1.4 billion. The change in the impact to the cattle sector was comparable to

the change under the wage-flexible scenario with the new data, with a decrease in sector

demand of $23.1 million.

These changes to the original database provide one extreme reference point to

how the model would be impacted by changing the data for the cost allocations within the

cattle sector. In order to provide an additional point of reference, we tripled the original

cost of the land factor for the model and redistributed the remaining factor costs to the

labor factor of production. The effects of these data changes were between the other two

scenarios and the full list model results with the new data as an imput to the model are

listed in Appendix C.

Under the wage-flexible scenario, the impact to household income for Floridians

increased from the $22 million loss in the original scenario to a loss of almost $43

million. The model showed that the impact to cattle sector demand would also increase

to $10 million from $5.5million. Under the fixed-wage scenario, the increase in the

household income impact increased by a similar scale from $330 million to $635 million.

Similar to the decrease in cattle sector demand under the wage-flexible scenario, the

impact to the demand from the cattle sector the under wage-fixed scenario also increased

to about $10 million.









These large variations in the output, resulting from changing the input data for the

cattle sector, demonstrate one limitation of this model in its sensitivity to this type of

change. As a result of this limitation, it is important that research is conducted to gather

data specifically on the cost strucutres of the land utilizing portion of the cattle ranching

industry in Florida. Increasing the accuracy of the data utilized to represent the cost

structure of the cattle industry in this CGE model would have a great impact on the

ability of the model to accurately determine economic impacts of shocks to the Florida

economy. The results of the output changes after changing the input data were discussed

to show the sensitivity of the model to this type of change, hence the results were not

discussed in the same level of detail as the original model. However, the full model

output sets for each of the two changes are provided in Appendicies B and C.

Research Opportunities

There are several areas for future research opportunities relating to this study. First

of all, a dynamic model, which has the ability to analyze the economic impacts to policy

shocks related to silvopasture with respect to time, could be investigated. This would give

policy makers more information on the time it would take for the effects to be felt by the

economy. It would also give more information on the smoothness of the transitions

experienced in the economy over time. Also, equations that capture the impacts that the

policy changes would have on the environment as well as environmental feedbacks into

the economy could be introduced into the CGE model. At the same time, a model could

be developed based on a utility function incorporating environmental variables as part of

the welfare of households, providing a more robust analysis of the well being of

households as a result of the sum of the economic and environmental changes they

experience.



















APPENDIX A
MODEL OUTPUT


Wage-flexible Scenario Output

SETS


No Name Size Description

1 SECT 5 Sectors
2 FAC 3 Factors
3 NUM SECT 1 sector 1
4 TWO SECT 4 sectors 2-5
5 NSEC SECT 1 sector 2
6 FORE SECT 1 sector 3
7 LAB FAC 1 Labor Factor of Production, factor 1
8 KD FAC 2 Capital and Land Factors, factors 2&3
9 LAND FAC 1 Land Factor of Production, factor 3

VARIABLES

No Name Size Arguments (if any) and Description

1 p Y 1 Total household expenditure
2 p PC 5 (SECT) Price of commodities
3 p PFL 1 (LAB FAC) Price of Labor
4 p PF 10 (KD FAC,SECT) Price of factors
5 p XCOM 5 (SECT) Total demand for (or supply of) commod ...
6 p XFACL 1 (LAB FAC) Total demand for (or supply of) factors
7 p XFACKD 2 (KD FAC) Total demand for (or supply of) factors
8 p XH 5 (SECT) Household consumption of commodities
9 p XC 25 (SECT,SECT) Intermediate commodity inputs
10 p XFL 5 (LAB FAC,SECT) Intermediate factor inputs
11 p XFKD 10 (KD FAC,SECT) Intermediate factor inputs
12 p XEXP 5 (SECT) Net Exports of commodities
13 p PW 5 (SECT) World Price of Commodity
14 p TLF 1 Total Labor Force
15 p ULF 1 Unemployed Labor Force
16 pT 1 Consumer Market Tax

TOTAL NUMBER OF exogenous VARIABLES IS 18.
THEY ARE AS FOLLOWS:

Just 5 of the 10 components of 'p PF' -- namely components:
1, 3, 5, 7, 9
All 1 components of 'p XFACL'
Just 5 of the 10 components of 'p XFKD' -- namely components:
2, 4, 6, 8, 10
All 5 components of 'p PW'
All 1 components of 'p TLF'
All 1 components of 'p T'

TOTAL NUMBER OF endogenous VARIABLES IS 65.
THEY ARE AS FOLLOWS:








56




All 1 components of 'p Y'
All 5 components of 'p PC'
All 1 components of 'p PFL'
Just 5 of the 10 components of 'p PF' --
2, 4, 6, 8, 10
All 5 components of 'p XCOM'
All 2 components of 'p XFACKD'
All 5 components of 'p XH'
All 25 components of 'p XC'
All 5 components of 'p XFL'
Just 5 of the 10 components of 'p XFKD'
1, 3, 5, 7, 9
All 5 components of 'p XEXP'
All 1 components of 'p ULF'

TOTAL NUMBER OF shocked VARIABLES IS 2.
THEY ARE AS FOLLOWS:

Just 1 of the 10 components of 'p PF' --
Just 1 of the 10 components of 'p XFKD'


namely components:







- namely components:


namely components: 1
- namely components: 2


THE SHOCKS ARE AS FOLLOWS.


p PF
1

p XFKD
2


SHOCK = 25.000000


SHOCK = -25.000000


END OF THE SHOCKS.


ALL THE endogenous VARIABLES ARE cumulatively-retained endogenous.

SHOCKS RELEVANT TO THE PRINT-OUT BELOW


p PF
1

p XFKD
2


SHOCK = 25.000000


SHOCK = -25.000000


THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE.

When levels values are available for a variable,
they are shown underneath the percent-change or change result.
The 4 results are shown in the order:
Percent-change (or change), Pre-simulation, Post-simulation, Change.

For example
3.000 (percent change)
500.0 (pre-sim level)
515.0 (post-sim level)
15.0 (change)

p Y Total household expenditure

-0.009218
240336.562000
240314.406000
-22.156250













p PC (SECT) Price of commodities


sl
3.034190
1.000000
1.030342
0.030342


s2
0.000000*
1.000000
1.000000
0.000000*


p PFL (LAB FAC)


Price of Labor


labor
-0.006444
1.000000
0.999936
-0.000064


p PF (KD FAC,SECT) Price of factors
p PF(-,sl) results where '-' is in set 'KD FAC'.

land
33.238327
1.000000
1.332383
0.332383

p PF(-,s2) results where '-' is in set 'KD FAC'.

land
-0.002865
1.000000
0.999971
-0.000029

p PF(-,s3) results where '-' is in set 'KD FAC'.

land
-0.000080
1.000000
0.999999
-0.000001

p PF(-,s4) results where '-' is in set 'KD FAC'.

land
-0.004617
1.000000
0.999954
-0.000046

p PF (KD FAC,SECT) Price of factors
p PF(-,s5) results where '-' is in set 'KD FAC'.

land
-0.006683
1.000000
0.999933
-0.000067

p XCOM (SECT) Total demand for (or supply of) commodities


s2 s3
-0.002865 0.000739


s3
0.000819
1.000000
0.999992
0.000008


s4
0.000595
1.000000
1.000006
0.000006


s5
-0.004364
1.000000
0.999956
-0.000044


sl
-2.947586


s4 s5
.0.005211 -0.002319












7694.735350
7694.515140
-0.220215


405.838989
405.841980
0.002991


54213.156200
54210.332000
-2.824219


416915.562000
416905.906000
-9.656250


p XFACKD (KD FAC) Total demand for (or supply of) factors


capital
-0.008778
102767.062000
102758.039000
-9.023438


p XH (SECT) Household consumption of commodities


-0.009218
1126.350950
1126.247070
-0.103882


0.008400
1.897000
1.896841
0.000159


s4
-0.009813
17089.293000
17087.615200
-1.677734


s5
-0.004854
222119.016000
222108.234000
-10.781250


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,sl) results where '-' is in set 'SECT'.


sl
-2.947586
41.120998
39.908920
-1.212078


s2
-0.002589
7.355000
7.354810
-0.000190


s3
0.001770
0.000000*
0.000000*
0.000000*


s4
-0.003183
3.335000
3.334894
-0.000106


p XC(-,s2) results where '-' is in set 'SECT'.


sl
-2.947854
9.930000
9.637279
-0.292722


s2
-0.002865
870.853027
870.828064
-0.024963


s3
0.002046
0.087000
0.086998
0.000002


s4
-0.003459
303.221008
303.210510
-0.010498


p XC(-,s3) results where '-' is in set 'SECT'.


sl
-2.945151
0.000000*
0.000000*
0.000000*


s2
-0.000080
122.128998
122.128899
-0.000099


s3
0.000739
0.315000
0.315002
0.000002


s4
-0.000674
5.378000
5.377964
-0.000036


s5
0.001776
43.889000
43.889778
0.000778




s5
0.001500
1131.000000
1131.016970
0.016968




s5
0.004285
52.792999
52.795261
0.002262


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,s4) results where '-' is in set 'SECT'.


s3
-0.003798
3.179000
3.178879
-0.000121


s4
-0.005211
8959.433590
8958.966800
-0.466797


p XC(-,s5) results where '-' is in set 'SECT'.


s3
0.005864
0.000000*
0.000000*
0.000000*


s4
-0.007278
7559.000000
7558.449710
-0.550293


s5
-0.000252
15060.677700
15060.639600
-0.038086




s5
-0.002319
99637.156200
99634.843800
-2.312500


p XFL (LAB FAC,SECT) Intermediate factor inputs


188.559998
183.002029
-5.557968


land
-0.245993
828.379639
826.341858
-2.037781


sl
-2.954021
0.000000*
0.000000*
0.000000*


sl
-2.949555
76.509003
74.252327
-2.256676


s2
-0.004617
748.979004
748.944397
-0.034607


sl
-2.951561
0.000000*
0.000000*
0.000000*


s2
-0.006683
849.520996
849.464233
-0.056763








59



p XFL(-,sl) results where '-' is in set 'LAB FAC'.

labor
0.003855
75.111000
75.113899
0.002899

p XFL(-,s2) results where '-' is in set 'LAB FAC'.

labor
0.003579
3541.367920
3541.494630
0.126709

p XFL(-,s3) results where '-' is in set 'LAB FAC'.

labor
0.006364
15.223000
15.223969
0.000969

p XFL(-,s4) results where '-' is in set 'LAB FAC'.

labor
0.001827
21113.478500
21113.863300
0.384766

p XFL(-,s5) results where '-' is in set 'LAB FAC'.

labor
-0.000240
215591.422000
215590.906000
-0.515625

p XFKD (KD FAC,SECT) Intermediate factor inputs
p XFKD(-,sl) results where '-' is in set 'KD FAC'.

capital
-20.031319
10.638600
8.507548
-2.131052

p XFKD(-,s2) results where '-' is in set 'KD FAC'.

capital
-0.002865
1102.939940
1102.908330
-0.031616

p XFKD(-,s3) results where '-' is in set 'KD FAC'.

capital
-0.000080
125.987000
125.986900








60


-0.000099

p XFKD(-,s4) results where '-' is in set 'KD FAC'.

capital
-0.004617
8250.000000
8249.619140
-0.380859

p XFKD(-,s5) results where '-' is in set 'KD FAC'.

capital
-0.006683
93277.500000
93271.265600
-6.234375

p XEXP (SECT) Net Exports of commodities

sl s2 s3 s4 s5
-2.945074 0.000000* 0.000819 -0.000595 0.004365
61.000000 3969.547120 400.360992 20293.494100 78871.039100
59.203506 3969.547120 400.364258 20293.373000 78874.484400
-1.796494 0.000000* 0.003265 -0.121094 3.445312

p ULF Unemployed Labor Force

0.000000*
3.900000
3.900000
0.000000*


Wage-rigid Scenario Output

SETS


No Name Size Description

1 SECT 5 Sectors
2 FAC 3 Factors
3 NUM SECT 1 sector 1
4 TWO SECT 4 sectors 2-5
5 NSEC SECT 1 sector 2
6 FORE SECT 1 sector 3
7 LAB FAC 1 Labor Factor of Production, factor 1
8 KD FAC 2 Capital and Land Factors, factors 2&3
9 LAND FAC 1 Land Factor of Production, factor 3

VARIABLES

No Name Size Arguments (if any) and Description

1 p Y 1 Total household expenditure
2 p PC 5 (SECT) Price of commodities
3 p PFL 1 (LAB FAC) Price of Labor
4 p PF 10 (KD FAC,SECT) Price of factors
5 p XCOM 5 (SECT) Total demand for (or supply of) commod ...
6 p XFACL 1 (LAB FAC) Total demand for (or supply of) factors
7 p XFACKD 2 (KD FAC) Total demand for (or supply of) factors
8 p XH 5 (SECT) Household consumption of commodities
9 p XC 25 (SECT,SECT) Intermediate commodity inputs












10 p XFL
11 p XFKD
12 p XEXP
13 p PW
14 p TLF
15 p ULF
16 pT


5 (LAB FAC,SECT) Intermediate factor inputs
10 (KD FAC,SECT) Intermediate factor inputs
5 (SECT) Net Exports of commodities
5 (SECT) World Price of Commodity
1 Total Labor Force
1 Unemployed Labor Force
1 Consumer Market Tax


THE SHOCKS ARE AS FOLLOWS.


p PF
1

p XFKD
2


SHOCK = 25.000000


SHOCK = -25.000000


END OF THE SHOCKS.

THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE.

p Y Total household expenditure

-0.137372
240336.562000
240006.406000
-330.156250

p PC (SECT) Price of commodities


sl
3.037269
1.000000
1.030373
0.030373


s2
0.000000*
1.000000
1.000000
0.000000*


s3
0.000163
1.000000
0.999998
0.000002


s4
0.005098
1.000000
1.000051
0.000051


s5
0.000121
1.000000
1.000001
0.000001


p PF (KD FAC,SECT) Price of factors
p PF(-,sl) results where '-' is in set 'KD FAC'.

land
33.190601
1.000000
1.331906
0.331906

p PF(-,s2) results where '-' is in set 'KD FAC'.

land
-0.042688
1.000000
0.999573
-0.000427

p PF(-,s3) results where '-' is in set 'KD FAC'.

land
-0.001191
1.000000
0.999988
-0.000012

p PF(-,s4) results where '-' is in set 'KD FAC'.








62



land
-0.068801
1.000000
0.999312
-0.000688

p PF(-,s5) results where '-' is in set 'KD FAC'.

land
-0.099595
1.000000
0.999004
-0.000996

p XCOM (SECT) Total demand for (or supply of) commodities


s2
-0.042688
7694.735350
7691.450680
-3.284668


s3
-0.001028
405.838989
405.834808
-0.004181


s4
-0.073895
54213.156200
54173.093800
-40.062500


s5
-0.099716
416915.562000
416499.844000
-415.718750


p XFACL (LAB FAC) Total demand for (or supply of) factors

labor
-0.096026
240336.594000
240105.812000
-230.781250

p XFACKD (KD FAC) Total demand for (or supply of) factors


capital
-0.098693
102767.062000
102665.641000
-101.421875


land
-0.245993
828.379639
826.341858
-2.037781


p XH (SECT) Household consumption of commodities


-0.137372
1126.350950
1124.803710
-1.547241


0.137209
1.897000
1.894397
0.002603


s4
-0.142463
17089.293000
17064.947300
-24.345703


s5
-0.137493
222119.016000
221813.609000
-305.406250


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,sl) results where '-' is in set 'SECT'.


sl
-2.985418
41.120998
39.893364
-1.227634


s2
-0.038578
7.355000
7.352163
-0.002837


s3
0.038414
0.000000*
0.000000*
0.000000*


s4
-0.043673
3.335000
3.333544
-0.001456


p XC(-,s2) results where '-' is in set 'SECT'.


sl
-2.989408
9.930000
9.633152
-0.296848


s2
-0.042688
870.853027
870.481262
-0.371765


s3
0.042525
0.087000
0.086963
0.000037


s4
-0.047783
303.221008
303.076111
-0.144897


s5
-0.038699
43.889000
43.872017
-0.016983




s5
-0.042809
1131.000000
1130.515870
-0.484131


sl
-2.985418
188.559998
182.930695
-5.629303


sl
-3.081308
0.000000*
0.000000*
0.000000*








63



p XC(-,s3) results where '-' is in set 'SECT'.


sl
-2.949130
0.000000*
0.000000*
0.000000*


s2
-0.001191
122.128998
122.127541
-0.001457


s3
0.001028
0.315000
0.314997
0.000003


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,s4) results where '-' is in set 'SECT'.


sl
-3.014753
76.509003
74.202446
-2.306557


s2
-0.068801
748.979004
748.463684
-0.515320


s3
-0.068638
3.179000
3.176818
-0.002182


p XC(-,s5) results where '-' is in set 'SECT'


sl
-3.044641
0.000000*
0.000000*
0.000000*


s2
-0.099595
849.520996
848.674927
-0.846069


s3
0.099432
0.000000*
0.000000*
0.000000*


s4
-0.073895
8959.433590
8952.813480
-6.620117




s4
-0.104687
7559.000000
7551.086910
-7.913086


s5
-0.068922
15060.677700
15050.297900
-10.379883




s5
-0.099716
99637.156200
99537.804700
-99.351563


p XFL (LAB FAC,SECT) Factor inputs
p XFL(-,sl) results where '-' is in set 'LAB FAC'.

labor
-0.038578
75.111000
75.082024
-0.028976

p XFL(-,s2) results where '-' is in set 'LAB FAC'.

labor
-0.042688
3541.367920
3539.856200
-1.511719

p XFL(-,s3) results where '-' is in set 'LAB FAC'.

labor
-0.001191
15.223000
15.222818
-0.000181

p XFL(-,s4) results where '-' is in set 'LAB FAC'.

labor
-0.068801
21113.478500
21098.953100
-14.525391

p XFL(-,s5) results where '-' is in set 'LAB FAC'.

labor
-0.099595
215591.422000


s4
-0.006288
5.378000
5.377662
-0.000338


s5
-0.001312
52.792999
52.792305
-0.000694








64



215376.703000
-214.718750


p XFKD (KD FAC,SECT) Factor inputs
p XFKD(-,sl) results where '-' is in set 'KD FAC'.

capital
-20.060226
10.638600
8.504473
-2.134128

p XFKD(-,s2) results where '-' is in set 'KD FAC'.

capital
-0.042688
1102.939940
1102.469120
-0.470825

p XFKD(-,s3) results where '-' is in set 'KD FAC'.

capital
-0.001191
125.987000
125.985497
-0.001503

p XFKD(-,s4) results where '-' is in set 'KD FAC'.

capital
-0.068801
8250.000000
8244.324220
-5.675781

p XFKD(-,s5) results where '-' is in set 'KD FAC'.

capital
-0.099595
93277.500000
93184.601600
-92.898438


p XEXP (SECT)


Net Exports of commodities


s2
0.000000*
3969.547120
3969.547120
0.000000*


s3
0.000163
400.360992
400.361633
0.000641


s4
-0.005097
20293.494100
20292.459000
-1.035156


s5
-0.000121
78871.039100
78870.945300
-0.093750


p ULF Unemployed Labor Force

1.842080
3.900000
3.971841
0.071841


sl
-2.947974
61.000000
59.201736
-1.798264



















APPENDIX B
ADJUSTED DATA SET MODEL OUTPUT ONE


Wage-flexible Scenario Output

SETS


No Name Size Description

1 SECT 5 Sectors
2 FAC 3 Factors
3 NUM SECT 1 sector 1
4 TWO SECT 4 sectors 2-5
5 NSEC SECT 1 sector 2
6 FORE SECT 1 sector 3
7 LAB FAC 1 Labor Factor of Production, factor 1
8 KD FAC 2 Capital and Land Factors, factors 2&3
9 LAND FAC 1 Land Factor of Production, factor 3

VARIABLES

No Name Size Arguments (if any) and Description

1 p Y 1 Total household expenditure
2 p PC 5 (SECT) Price of commodities
3 p PFL 1 (LAB FAC) Price of Labor
4 p PF 10 (KD FAC,SECT) Price of factors
5 p XCOM 5 (SECT) Total demand for (or supply of) commod ...
6 p XFACL 1 (LAB FAC) Total demand for (or supply of) factors
7 p XFACKD 2 (KD FAC) Total demand for (or supply of) factors
8 p XH 5 (SECT) Household consumption of commodities
9 p XC 25 (SECT,SECT) Intermediate commodity inputs
10 p XFL 5 (LAB FAC,SECT) Intermediate factor inputs
11 p XFKD 10 (KD FAC,SECT) Intermediate factor inputs
12 p XEXP 5 (SECT) Net Exports of commodities
13 p PW 5 (SECT) World Price of Commodity
14 p TLF 1 Total Labor Force
15 p ULF 1 Unemployed Labor Force
16 pT 1 Consumer Market Tax



THE SHOCKS ARE AS FOLLOWS.

p PF
1 SHOCK = 25.000000

p XFKD
2 SHOCK = -25.000000

END OF THE SHOCKS.

ALL THE endogenous VARIABLES ARE cumulatively-retained endogenous.








66






SHOCKS RELEVANT TO THE PRINT-OUT BELOW


p PF
1

p XFKD
2


SHOCK = 25.000000


SHOCK = -25.000000


THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE.

p Y Total household expenditure

-0.039933
240336.562000
240240.594000
-95.968750

p PC (SECT) Price of commodities


sl
13.819850
1.000000
1.138198
0.138198


s2
0.000000*
1.000000
1.000000
0.000000*


s3
0.003548
1.000000
0.999965
0.000035


s4
0.002576
1.000000
1.000026
0.000026


s5
-0.018910
1.000000
0.999811
-0.000189


p PFL (LAB FAC) Price of Labor

labor
-0.027917
1.000000
0.999721
-0.000279


p PF (KD FAC,SECT) Price of factors
p PF(-,sl) results where '-' is in set 'KD FAC'.

land
33.226902
1.000000
1.332269
0.332269

p PF(-,s2) results where '-' is in set 'KD FAC'.

land
-0.012409
1.000000
0.999876
-0.000124

p PF(-,s3) results where '-' is in set 'KD FAC'.

land
-0.000346
1.000000
0.999997
-0.000003

p PF(-,s4) results where '-' is in set 'KD FAC'.








67




land
-0.020000
1.000000
0.999800
-0.000200

p PF (KD FAC,SECT) Price of factors
p PF(-,s5) results where '-' is in set 'KD FAC'.

land
-0.028951
1.000000
0.999711
-0.000289

p XCOM (SECT) Total demand for (or supply of) commodities


s2
-0.012409
7694.735350
7693.780270
-0.955078


s3
0.003201
405.838989
405.851990
0.013000


s4
-0.022575
54213.156200
54200.918000
-12.238281


s5
-0.010044
416915.562000
416873.688000
-41.875000


p XFACKD (KD FAC) Total demand for (or supply of) factors


capital
-0.030328
102767.062000
102735.898000
-31.164062


p XH (SECT) Household consumption of commodities


s2
-0.039933
1126.350950
1125.901120
-0.449829


s3
0.036387
1.897000
1.896310
0.000690


s4
-0.042507
17089.293000
17082.029300
-7.263672


s5
-0.021027
222119.016000
222072.312000
-46.703125


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,sl) results where '-' is in set 'SECT'.


sl
-12.159436
41.120998
36.120918
-5.000080


s2
-0.011214
7.355000
7.354175
-0.000825


s3
0.007667
0.000000*
0.000000*
0.000000*


s4
-0.013789
3.335000
3.334540
-0.000460


p XC(-,s2) results where '-' is in set 'SECT'.


sl
-12.160485
9.930000
8.722464
-1.207537


s2
-0.012409
870.853027
870.744934
-0.108093


s3
0.008862
0.087000
0.086992
0.000008


s4
-0.014984
303.221008
303.175568
-0.045441


p XC(-,s3) results where '-' is in set 'SECT'.


sl
-12.149884
0.000000*
0.000000*


s2
-0.000346
122.128998
122.128578


s3
0.003201
0.315000
0.315010


s4
-0.002922
5.378000
5.377842


s5
0.007697
43.889000
43.892380
0.003380




s5
0.006501
1131.000000
1131.073490
0.073486




s5
0.018567
52.792999
52.802803


sl
-12.159436
188.559998
165.632172
-22.927826


land
-1.885813
879.490234
862.904663
-16.585571


sl
-12.184669
0.000000*
0.000000*
0.000000*












0.000000*


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,s4) results where '-' is in set 'SECT'.


sl
-12.167155
76.509003
67.200035
-9.308968


s2
-0.020000
748.979004
748.829224
-0.149780


s3
-0.016453
3.179000
3.178477
-0.000523


s4
-0.022575
8959.433590
8957.411130
-2.022461


p XC(-,s5) results where '-' is in set 'SECT'.


sl
-12.175018
0.000000*
0.000000*
0.000000*


s2
-0.028951
849.520996
849.275024
-0.245972


s3
0.025405
0.000000*
0.000000*
0.000000*


s4
-0.031526
7559.000000
7556.616700
-2.383301


s5
-0.001091
15060.677700
15060.513700
-0.164062




s5
-0.010044
99637.156200
99627.148400
-10.007812


p XFL (LAB FAC,SECT) Intermediate factor inputs
p XFL(-,sl) results where '-' is in set 'LAB FAC'.

labor
0.016708
24.000000
24.004009
0.004009

p XFL(-,s2) results where '-' is in set 'LAB FAC'.

labor
0.015512
3541.367920
3541.917240
0.549316

p XFL(-,s3) results where '-' is in set 'LAB FAC'.

labor
0.027579
15.223000
15.227198
0.004198

p XFL(-,s4) results where '-' is in set 'LAB FAC'.

labor
0.007920
21113.478500
21115.150400
1.671875

p XFL(-,s5) results where '-' is in set 'LAB FAC'.

labor
-0.001034
215591.422000
215589.188000
-2.234375

p XFKD (KD FAC,SECT) Intermediate factor inputs
p XFKD(-,sl) results where '-' is in set 'KD FAC'.


-0.000420


0.000010


-0.000157


0.009804








69



capital
-20.038233
10.638600
8.506813
-2.131787

p XFKD(-,s2) results where '-' is in set 'KD FAC'.

capital
-0.012409
1102.939940
1102.803100
-0.136841

p XFKD(-,s3) results where '-' is in set 'KD FAC'.

capital
-0.000346
125.987000
125.986565
-0.000435

p XFKD(-,s4) results where '-' is in set 'KD FAC'.

capital
-0.020000
8250.000000
8248.349610
-1.650391

p XFKD(-,s5) results where '-' is in set 'KD FAC'.

capital
-0.028951
93277.500000
93250.492200
-27.007812


p XEXP (SECT)


Net Exports of commodities


s2
0.000000*
3969.547120
3969.547120
0.000000*


s3
0.003548
400.360992
400.375183
0.014191


s4
-0.002575
20293.494100
20292.970700
-0.523438


s5
0.018913
78871.039100
78885.953100
14.914062


Wage-fixed Scenario Output


SETS


Size Description


Sectors
Factors
sector 1
sectors 2-5
sector 2
sector 3
Labor Factor of Production, factor 1
Capital and Land Factors, factors 2&3
Land Factor of Production, factor 3


sl
-12.149580
61.000000
53.588757
-7.411243


No Name


SECT
FAC
NUM SECT
TWO SECT
NSEC SECT
FORE SECT
LAB FAC
KD FAC
LAND FAC








70




VARIABLES

No Name Size Arguments (if any) and Description

1 p Y 1 Total household expenditure
2 p PC 5 (SECT) Price of commodities
3 p PFL 1 (LAB FAC) Price of Labor
4 p PF 10 (KD FAC,SECT) Price of factors
5 p XCOM 5 (SECT) Total demand for (or supply of) commod ...
6 p XFACL 1 (LAB FAC) Total demand for (or supply of) factors
7 p XFACKD 2 (KD FAC) Total demand for (or supply of) factors
8 p XH 5 (SECT) Household consumption of commodities
9 p XC 25 (SECT,SECT) Intermediate commodity inputs
10 p XFL 5 (LAB FAC,SECT) Intermediate factor inputs
11 p XFKD 10 (KD FAC,SECT) Intermediate factor inputs
12 p XEXP 5 (SECT) Net Exports of commodities
13 p PW 5 (SECT) World Price of Commodity
14 p TLF 1 Total Labor Force
15 p ULF 1 Unemployed Labor Force
16 pT 1 Consumer Market Tax



THE SHOCKS ARE AS FOLLOWS.


p PF
1 SHOCK = 25.000000

p XFKD
2 SHOCK = -25.000000

END OF THE SHOCKS.

THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE.




p Y Total household expenditure

-0.591877
240336.562000
238914.062000
-1422.500000

p PC (SECT) Price of commodities

sl s2 s3 s4 s5
13.762493 0.000000* -0.000703 0.021981 0.000522
1.000000 1.000000 1.000000 1.000000 1.000000
1.137625 1.000000 0.999993 1.000220 1.000005
0.137625 0.000000* -0.000007 0.000220 0.000005

p PF (KD FAC,SECT) Price of factors
p PF(-,sl) results where '-' is in set 'KD FAC'.

land
33.021626
1.000000
1.330216
0.330216








71



p PF(-,s2) results where '-' is in set 'KD FAC'.

land
-0.183923
1.000000
0.998161
-0.001839

p PF(-,s3) results where '-' is in set 'KD FAC'.

land
-0.005132
1.000000
0.999949
-0.000051

p PF(-,s4) results where '-' is in set 'KD FAC'.

land
-0.296434
1.000000
0.997036
-0.002964

p PF(-,s5) results where '-' is in set 'KD FAC'.

land
-0.429110
1.000000
0.995709
-0.004291


p XCOM (SECT)


Total demand for (or supply of) commodities


s2
-0.183923
7694.735350
7680.583010
-14.152344


s3
-0.004429
405.838989
405.821014
-0.017975


s4
-0.318346
54213.156200
54040.570300
-172.585938


s5
-0.429630
416915.562000
415124.375000
-1791.187500


p XFACL (LAB FAC) Total demand for (or supply of) factors

labor
-0.413785
240285.500000
239291.234000
-994.265625

p XFACKD (KD FAC) Total demand for (or supply of) factors


capital
-0.417583
102767.062000
102337.922000
-429.140625


land
-1.885812
879.490234
862.904724
-16.585510


p XH (SECT) Household consumption of commodities


-0.591877
1126.350950
1119.684330


0.591178
1.897000
1.885785


s4
-0.613724
17089.293000
16984.412100


s5
-0.592396
222119.016000
220803.188000


sl
-12.251341
188.559998
165.458862
-23.101135


sl
-12.625557
0.000000*
0.000000*












0.011215 -104.880859


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,sl) results where '-' is in set 'SECT'.


sl
-12.251341
41.120998
36.083126
-5.037872


s2
-0.166210
7.355000
7.342775
-0.012225


s3
0.165508
0.000000*
0.000000*
0.000000*


s4
-0.188150
3.335000
3.328725
-0.006275


p XC(-,s2) results where '-' is in set 'SECT'.


sl
-12.266912
9.930000
8.711896
-1.218104


s2
-0.183923
870.853027
869.251343
-1.601685


s3
0.183221
0.087000
0.086841
0.000159


s4
-0.205859
303.221008
302.596802
-0.624207


p XC(-,s3) results where '-' is in set 'SECT'.


sl
-12.109735
0.000000*
0.000000*
0.000000*


s2
-0.005132
122.128998
122.122726
-0.006271


s3
0.004429
0.315000
0.314986
0.000014


s4
-0.027107
5.378000
5.376542
-0.001458


s5
-0.166731
43.889000
43.815823
-0.073177




s5
-0.184445
1131.000000
1128.913940
-2.086060




s5
-0.005654
52.792999
52.790012
-0.002987


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,s4) results where '-' is in set 'SECT'.


s2
-0.296434
748.979004
746.758789
-2.220215


s3
-0.295733
3.179000
3.169599
-0.009401


s4
-0.318346
8959.433590
8930.912110
-28.521484


p XC(-,s5) results where '-' is in set 'SECT'.


s2
-0.429110
849.520996
845.875610
-3.645386


s3
0.428409
0.000000*
0.000000*
0.000000*


s4
-0.450992
7559.000000
7524.909670
-34.090332


s5
-0.296955
15060.677700
15015.954100
-44.723633




s5
-0.429630
99637.156200
99209.085900
-428.070312


p XFL (LAB FAC,SECT) Intermediate factor inputs
p XFL(-,sl) results where '-' is in set 'LAB FAC'.

labor
-0.166210
24.000000
23.960110
-0.039890

p XFL(-,s2) results where '-' is in set 'LAB FAC'.


labor
-0.183923
3541.367920
3534.854490
-6.513428


p XFL(-,s3) results where '-' is in set 'LAB FAC'.


sl
-12.365824
76.509003
67.048035
-9.460968


sl
-12.482463
0.000000*
0.000000*
0.000000*


0.000000*


-6.666626


1315.828120








73




labor
-0.005132
15.223000
15.222219
-0.000781

p XFL(-,s4) results where '-' is in set 'LAB FAC'.

labor
-0.296434
21113.478500
21050.890600
-62.587891

p XFL(-,s5) results where '-' is in set 'LAB FAC'.

labor
-0.429110
215591.422000
214666.297000
-925.125000


p XFKD (KD FAC,SECT) Intermediate factor inputs
p XFKD(-,sl) results where '-' is in set 'KD FAC'.

capital
-20.162603
10.638600
8.493582
-2.145019

p XFKD(-,s2) results where '-' is in set 'KD FAC'.

capital
-0.183923
1102.939940
1100.911380
-2.028564

p XFKD(-,s3) results where '-' is in set 'KD FAC'.

capital
-0.005132
125.987000
125.980537
-0.006462

p XFKD(-,s4) results where '-' is in set 'KD FAC'.

capital
-0.296434
8250.000000
8225.543950
-24.456055

p XFKD(-,s5) results where '-' is in set 'KD FAC'.

capital
-0.429110
93277.500000
92877.234400








74



-400.265625

p XEXP (SECT) Net Exports of commodities


s2
0.000000*
3969.547120
3969.547120
0.000000*


s3
0.000703
400.360992
400.363800
0.002808


s4
-0.021976
20293.494100
20289.035200
-4.458984


s5
-0.000522
78871.039100
78870.625000
-0.414062


p ULF Unemployed Labor Force

8.194535
3.900000
4.219587
0.319587


sl
-12.105221
61.000000
53.615814
-7.384186



















APPENDIX C
ADJUSTED DATA SET MODEL OUTPUT TWO


Wage-flexible Scenario Output

SETS


No Name Size Description

1 SECT 5 Sectors
2 FAC 3 Factors
3 NUM SECT 1 sector 1
4 TWO SECT 4 sectors 2-5
5 NSEC SECT 1 sector 2
6 FORE SECT 1 sector 3
7 LAB FAC 1 Labor Factor of Production, factor 1
8 KD FAC 2 Capital and Land Factors, factors 2&3
9 LAND FAC 1 Land Factor of Production, factor 3

VARIABLES

No Name Size Arguments (if any) and Description

1 p Y 1 Total household expenditure
2 p PC 5 (SECT) Price of commodities
3 p PFL 1 (LAB FAC) Price of Labor
4 p PF 10 (KD FAC,SECT) Price of factors
5 p XCOM 5 (SECT) Total demand for (or supply of) commod ...
6 p XFACL 1 (LAB FAC) Total demand for (or supply of) factors
7 p XFACKD 2 (KD FAC) Total demand for (or supply of) factors
8 p XH 5 (SECT) Household consumption of commodities
9 p XC 25 (SECT,SECT) Intermediate commodity inputs
10 p XFL 5 (LAB FAC,SECT) Intermediate factor inputs
11 p XFKD 10 (KD FAC,SECT) Intermediate factor inputs
12 p XEXP 5 (SECT) Net Exports of commodities
13 p PW 5 (SECT) World Price of Commodity
14 p TLF 1 Total Labor Force
15 p ULF 1 Unemployed Labor Force
16 pT 1 Consumer Market Tax


THE SHOCKS ARE AS FOLLOWS.

p PF
1 SHOCK = 25.000000

p XFKD
2 SHOCK = -25.000000

END OF THE SHOCKS.

THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE.













p Y Total household expenditure

-0.017755
240336.562000
240293.891000
-42.671875

p PC (SECT) Price of commodities


sl
5.925771
1.000000
1.059258
0.059258


s2
0.000000*
1.000000
1.000000
0.000000*


p PFL (LAB FAC)


Price of Labor


labor
-0.012412
1.000000
0.999876
-0.000124


p PF (KD FAC,SECT) Price of factors
p PF(-,sl) results where '-' is in set 'KD FAC'.

land
33.235149
1.000000
1.332351
0.332351

p PF(-,s2) results where '-' is in set 'KD FAC'.

land
-0.005517
1.000000
0.999945
-0.000055

p PF(-,s3) results where '-' is in set 'KD FAC'.

land
-0.000154
1.000000
0.999998
-0.000002

p PF(-,s4) results where '-' is in set 'KD FAC'.

land
-0.008892
1.000000
0.999911
-0.000089

p PF (KD FAC,SECT) Price of factors
p PF(-,s5) results where '-' is in set 'KD FAC'.

land
-0.012873
1.000000


s3
0.001577
1.000000
0.999984
0.000016


s4
0.001145
1.000000
1.000011
0.000011


s5
-0.008407
1.000000
0.999916
-0.000084








77



0.999871
-0.000129

p XCOM (SECT) Total demand for (or supply of) commodities


s2
-0.005517
7694.735350
7694.311040
-0.424316


s3
0.001423
405.838989
405.844757
0.005768


s4
-0.010037
54213.156200
54207.714800
-5.441406


s5
-0.004466
416915.562000
416896.938000
-18.625000


p XFACKD (KD FAC) Total demand for (or supply of) factors


capital
-0.014767
102767.062000
102751.883000
-15.179688


p XH (SECT) Household consumption of commodities


s2
-0.017755
1126.350950
1126.151000
-0.199951


s3
0.016178
1.897000
1.896693
0.000307


s4
-0.018900
17089.293000
17086.062500
-3.230469


s5
-0.009349
222119.016000
222098.250000
-20.765625


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,sl) results where '-' is in set 'SECT'.


sl
-5.600160
41.120998
38.818157
-2.302841


s2
-0.004985
7.355000
7.354633
-0.000367


s3
0.003408
0.000000*
0.000000*
0.000000*


s4
-0.006130
3.335000
3.334796
-0.000204


p XC(-,s2) results where '-' is in set 'SECT'.


sl
-5.600662
9.930000
9.373855
-0.556146


s2
-0.005517
870.853027
870.804993
-0.048035


s3
0.003940
0.087000
0.086997
0.000003


s4
-0.006662
303.221008
303.200806
-0.020203


p XC(-,s3) results where '-' is in set 'SECT'.


sl
-5.595598
0.000000*
0.000000*
0.000000*


s2
-0.000154
122.128998
122.128807
-0.000191


s3
0.001423
0.315000
0.315004
0.000004


s4
-0.001299
5.378000
5.377930
-0.000070


s5
0.003422
43.889000
43.890503
0.001503




s5
0.002890
1131.000000
1131.032710
0.032715




s5
0.008253
52.792999
52.797356
0.004356


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,s4) results where '-' is in set 'SECT'.


s3
-0.007315
3.179000
3.178767
-0.000232


s4
-0.010037
8959.433590
8958.534180
-0.899414


s5
-0.000486
15060.677700
15060.604500
-0.073242


p XC(-,s5) results where '-' is in set 'SECT'.


sl
-5.600160
188.559998
178.000336
-10.559662


land
-0.724580
842.587219
836.481995
-6.105225


sl
-5.612217
0.000000*
0.000000*
0.000000*


sl
-5.603850
76.509003
72.221550
-4.287453


s2
-0.008892
748.979004
748.912415
-0.066589













sl
-5.607608
0.000000*
0.000000*
0.000000*


-0.012873
849.520996
849.411621
-0.109375


0.011296
0.000000*
0.000000*
0.000000*


s4
-0.014018
7559.000000
7557.940430
-1.059570


s5
-0.004466
99637.156200
99632.703100
-4.453125


p XFL (LAB FAC,SECT) Intermediate factor inputs
p XFL(-,sl) results where '-' is in set 'LAB FAC'.

labor
0.007427
60.900002
60.904526
0.004524

p XFL(-,s2) results where '-' is in set 'LAB FAC'.

labor
0.006895
3541.367920
3541.612060
0.244141

p XFL(-,s3) results where '-' is in set 'LAB FAC'.

labor
0.012259
15.223000
15.224866
0.001866

p XFL(-,s4) results where '-' is in set 'LAB FAC'.

labor
0.003520
21113.478500
21114.220700
0.742188

p XFL(-,s5) results where '-' is in set 'LAB FAC'.

labor
-0.000461
215591.422000
215590.422000
-1.000000


p XFKD (KD FAC,SECT) Intermediate factor inputs
p XFKD(-,sl) results where '-' is in set 'KD FAC'.

capital
-20.033243
10.638600
8.507343
-2.131257

p XFKD(-,s2) results where '-' is in set 'KD FAC'.

capital
-0.005517
1102.939940








79


1102.879150
-0.060791

p XFKD(-,s3) results where '-' is in set 'KD FAC'.

capital
-0.000154
125.987000
125.986809
-0.000191

p XFKD(-,s4) results where '-' is in set 'KD FAC'.

capital
-0.008892
8250.000000
8249.266600
-0.733398

p XFKD(-,s5) results where '-' is in set 'KD FAC'.

capital
-0.012873
93277.500000
93265.492200
-12.007812

p XEXP (SECT) Net Exports of commodities

sl s2 s3 s4 s5
-5.595454 0.000000* 0.001577 -0.001145 0.008407
61.000000 3969.547120 400.360992 20293.494100 78871.039100
57.586773 3969.547120 400.367310 20293.261700 78877.671900
-3.413227 0.000000* 0.006317 -0.232422 6.632812


Wage-fixed Scenario Output

SETS


No Name Size Description

1 SECT 5 Sectors
2 FAC 3 Factors
3 NUM SECT 1 sector 1
4 TWO SECT 4 sectors 2-5
5 NSEC SECT 1 sector 2
6 FORE SECT 1 sector 3
7 LAB FAC 1 Labor Factor of Production, factor 1
8 KD FAC 2 Capital and Land Factors, factors 2&3
9 LAND FAC 1 Land Factor of Production, factor 3

VARIABLES

No Name Size Arguments (if any) and Description

1 p Y 1 Total household expenditure
2 p PC 5 (SECT) Price of commodities
3 p PFL 1 (LAB FAC) Price of Labor
4 p PF 10 (KD FAC,SECT) Price of factors
5 p XCOM 5 (SECT) Total demand for (or supply of) commod ...
6 p XFACL 1 (LAB FAC) Total demand for (or supply of) factors












7 p XFACKD
8 p XH
9 p XC
10 p XFL
11 p XFKD
12 p XEXP
13 p PW
14 p TLF
15 p ULF
16 pT


2 (KD FAC) Total demand for (or supply of) factors
5 (SECT) Household consumption of commodities
25 (SECT,SECT) Intermediate commodity inputs
5 (LAB FAC,SECT) Intermediate factor inputs
10 (KD FAC,SECT) Intermediate factor inputs
5 (SECT) Net Exports of commodities
5 (SECT) World Price of Commodity
1 Total Labor Force
1 Unemployed Labor Force
1 Consumer Market Tax


THE SHOCKS ARE AS FOLLOWS.


p PF
1

p XFKD
2


SHOCK = 25.000000


SHOCK = -25.000000


END OF THE SHOCKS.

THE RESULTS BELOW ARE CUMULATIVE EFFECTS OF ALL SHOCKS ABOVE.

p Y Total household expenditure

-0.264188
240336.562000
239701.625000
-634.937500

p PC (SECT) Price of commodities


sl
5.923539
1.000000
1.059235
0.059235


s2
0.000000*
1.000000
1.000000
0.000000*


s3
0.000314
1.000000
0.999997
0.000003


s4
0.009806
1.000000
1.000098
0.000098


s5
0.000233
1.000000
1.000002
0.000002


p PF (KD FAC,SECT) Price of factors
p PF(-,sl) results where '-' is in set 'KD FAC'.

land
33.143456
1.000000
1.331435
0.331435

p PF(-,s2) results where '-' is in set 'KD FAC'.

land
-0.082095
1.000000
0.999179
-0.000821

p PF(-,s3) results where '-' is in set 'KD FAC'.

land
-0.002291
1.000000
0.999977
-0.000023








81




p PF(-,s4) results where '-' is in set 'KD FAC'.

land
-0.132315
1.000000
0.998677
-0.001323

p PF(-,s5) results where '-' is in set 'KD FAC'.

land
-0.191536
1.000000
0.998085
-0.001915

p XCOM (SECT) Total demand for (or supply of) commodities


s2
-0.082095
7694.735350
7688.418460
-6.316895


s3
-0.001977
405.838989
405.830963
-0.008026


s4
-0.142107
54213.156200
54136.117200
-77.039063


s5
-0.191769
416915.562000
416116.062000
-799.500000


p XFACL (LAB FAC) Total demand for (or supply of) factors

labor
-0.184679
240322.391000
239878.562000
-443.828125

p XFACKD (KD FAC) Total demand for (or supply of) factors


capital
-0.187670
102767.062000
102574.203000
-192.859375


land
-0.724580
842.587219
836.481995
-6.105225


p XH (SECT) Household consumption of commodities


-0.264188
1126.350950
1123.375240
-2.975708


0.263875
1.897000
1.891994
0.005006


s4
-0.273967
17089.293000
17042.474600
-46.818359


s5
-0.264421
222119.016000
221531.688000
-587.328125


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,sl) results where '-' is in set 'SECT'.


sl
-5.663516
41.120998
38.792103
-2.328896


s2
-0.074191
7.355000
7.349543
-0.005457


s3
0.073877
0.000000*
0.000000*
0.000000*


p XC(-,s2) results where '-' is in set 'SECT'.


sl
-5.670979
9.930000


s2
-0.082095
870.853027


s3
0.081782
0.087000


s4
-0.083988
3.335000
3.332199
-0.002801




s4
-0.091892
303.221008


s5
-0.074424
43.889000
43.856335
-0.032665




s5
-0.082328
1131.000000


sl
-5.663516
188.559998
177.880875
-10.679123


sl
-5.842914
0.000000*
0.000000*
0.000000*












9.366872
-0.563128


870.138123
-0.714905


0.086929
0.000071


302.942383
-0.278625


p XC(-,s3) results where '-' is in set 'SECT'.


sl
-5.595626
0.000000*
0.000000*
0.000000*


s2
-0.002291
122.128998
122.126198
-0.002800


s3
0.001977
0.315000
0.314994
0.000006


s4
-0.012095
5.378000
5.377349
-0.000650


1130.068850
-0.931152




s5
-0.002524
52.792999
52.791668
-0.001331


p XC (SECT,SECT) Intermediate commodity inputs
p XC(-,s4) results where '-' is in set 'SECT'.


sl
-5.718398
76.509003
72.133911
-4.375092


s2
-0.132315
748.979004
747.987976
-0.991028


s3
-0.132002
3.179000
3.174803
-0.004196


s4
-0.142107
8959.433590
8946.701170
-12.732422


p XC(-,s5) results where '-' is in set 'SECT'.


sl
-5.774314
0.000000*
0.000000*
0.000000*


s2
-0.191536
849.520996
847.893860
-1.627136


s3
0.191223
0.000000*
0.000000*
0.000000*


s4
-0.201322
7559.000000
7543.782230
-15.217773


s5
-0.132548
15060.677700
15040.714800
-19.962891




s5
-0.191769
99637.156200
99446.085900
-191.070313


p XFL (LAB FAC,SECT) Intermediate factor inputs
p XFL(-,sl) results where '-' is in set 'LAB FAC'.

labor
-0.074191
60.900002
60.854820
-0.045181

p XFL(-,s2) results where '-' is in set 'LAB FAC'.

labor
-0.082095
3541.367920
3538.460690
-2.907227

p XFL(-,s3) results where '-' is in set 'LAB FAC'.

labor
-0.002291
15.223000
15.222651
-0.000349

p XFL(-,s4) results where '-' is in set 'LAB FAC'.


labor
-0.132315
21113.478500
21085.543000
-27.935547


p XFL(-,s5) results where '-' is in set 'LAB FAC'.








83



labor
-0.191536
215591.422000
215178.484000
-412.937500


p XFKD (KD FAC,SECT) Intermediate factor inputs
p XFKD(-,sl) results where '-' is in set 'KD FAC'.

capital
-20.088785
10.638600
8.501434
-2.137166

p XFKD(-,s2) results where '-' is in set 'KD FAC'.

capital
-0.082095
1102.939940
1102.034420
-0.905518

p XFKD(-,s3) results where '-' is in set 'KD FAC'.

capital
-0.002291
125.987000
125.984116
-0.002884

p XFKD(-,s4) results where '-' is in set 'KD FAC'.

capital
-0.132315
8250.000000
8239.083980
-10.916016

p XFKD(-,s5) results where '-' is in set 'KD FAC'.

capital
-0.191536
93277.500000
93098.843800
-178.656250


p XEXP (SECT)


Net Exports of commodities


s2
0.000000*
3969.547120
3969.547120
0.000000*


s3
0.000314
400.360992
400.362244
0.001251


s4
-0.009805
20293.494100
20291.503900
-1.990234


s5
-0.000233
78871.039100
78870.851600
-0.187500


p ULF Unemployed Labor Force
3.574265
3.900000
4.039396
0.139396


sl
-5.593462
61.000000
57.587986
-3.412014
















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BIOGRAPHICAL SKETCH

Troy Thomas Timko was born in 1975, in Lake Wales, Florida. He grew up in the

small town of Sebring, Florida. After graduating from high school, he joined the United

States Navy for a 6-year tour of duty as a nuclear reactor operator. Upon leaving the navy

in 1999, he returned to college. In December 2002, he graduated with his bachelor's

degree from the Warrington College of Business Administration, at the University of

Florida. He then continued his education at the School of Forest Resources and

Conservation, at the University of Florida, in order to obtain his Master of Science

degree.