Aflatoxin Contamination and Regulation Policy Interventions: Economic Implications for Peanut Market Participants by Michael Agyekum A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama December 14, 2013 Keywords: aflatoxins, food standards regulation, equilibrium displacement modeling, price effects, economic welfare, peanut trade Copyright 2013 by Michael Agyekum Approved by Curtis Jolly, Chair, Professor of Agricultural Economics and Rural Sociology Henry Kinnucan, Professor of Agricultural Economics and Rural Sociology Norbert Wilson, Associate Professor of Agricultural Economics and Rural Sociology Asheber Abebe, Associate Professor of Mathematics and Statistics ii Abstract This dissertation is composed of three chapters covering topics about the impact of aflatoxin regulation policies on peanut suppliers and consumers. Chapter One evaluates welfare implications of aflatoxin standards imposed on peanut imported into the European Union (EU) market. Price and quantity effects on peanut suppliers and consumers are determined. The equilibrium displacement modeling (EDM) approach is applied on a source-differentiated market; where standards compliance costs are modeled as import tax to understand the distribution of economic incidence. Findings show that aflatoxin regulation tightening leads to price and quantity drop for the United States and other exporters, while China benefits owing to price and quantity increases. This result contradicts popular belief that strict aflatoxin regulations hurt all exporters in terms of lost revenues. Also, import suppliers and consumers share in the costs from the policy although consumers pay much of the costs. Chapter Two isolates the peanut industry in Ghana as a specific-country case and examines the distribution of economic impacts on domestic producers and consumers after incorporating important market features; namely trade status, and consumer demand for quality peanut. The EDM technique is employed in three nested models; autarkic peanut sector, small exporter with supply shift, and small exporter with both supply and demand shifts. Results from the autarkic model shows that domestic consumers experience greater economic loss. In the export model with iii supply shift only, producers bear the full economic burden due to Ghana?s status as a small peanut trader. However, the third and more generalized model reveals that although producers bear the entire cost of the intervention, they could actually gain if consumer demand for quality peanut is incorporated into the analyses. Although Chapter Two incorporates demand for quality peanut, the analysis is based on strong assumptions about consumer preference for safer peanut. Therefore, Chapter Three provides empirical evidence of consumers? willingness to pay (WTP) for quality peanut. Contingent Valuation survey was carried out on 652 individuals in Ghana. The study employs a semi-double-bounded dichotomous choice method based on random utility theory. Results indicate that 79% of consumers are willing to pay more for peanut with reduced aflatoxin levels; premiums range from 13% to 66%. Also, high income households, smaller family sizes, and younger people are more willing to pay for aflatoxin-free peanut than their counterparts. Interestingly, consumer characteristics such as region of residence, aflatoxin awareness, and one?s level of formal education are found to have no influence on WTP. These findings are important to the research community and regulatory bodies regarding holistic assessments of aflatoxin interventions. iv Acknowledgments I would like to thank my advisor and committee chair, Dr. Curtis M. Jolly, for being an excellent mentor, father, ?grand master?, and an incredibly good friend. Dr. Jolly has been extremely supportive throughout my stay in Auburn and I cannot thank him enough. May the good Lord continue to bless you and your wonderful wife, Dr. Pauline Jolly. I have benefited immensely from your guidance and inspiration. I would also like to express my heartfelt gratitude to Dr. Henry W. Kinnucan for his invaluable contributions toward the successful completion of this dissertation. Dr. Kinnucan has been another great source of inspiration, support, and encouragement, and I appreciate you serving on my dissertation advisory committee. My appreciation also goes to Dr. Norbert Wilson and Dr. Asheber Abebe for agreeing to serve on my dissertation committee and providing insightful comments and critiques that have contributed to the completion of this work. I owe you all tons of gratitude. Next, I am grateful to my parents, Mrs. Janet Bema and Mr. Emmanuel Apeadu, for their prayers, unconditional love and support throughout my life. Also, my siblings; Eric Yeboah, Andrews Agyekum, Gloria Tweneboah, Rita Agyekum, and Francisca O. Agyekum deserve special recognition for their love and sacrifices for me. I would also like to thank my lovely wife, Mrs. Dorcas Agyekum, for her amazing love and understanding. My close friends; Asare Twum Barima and Romeo O. Akrasi have also played pivotal roles in my life. Similarly, I appreciate the v friendship of my colleagues in the Department of Agricultural Economics and Rural Sociology (DAERS), as well as all family and friends who are not explicitly mentioned here due to space limitation. Ms. Kathleen Dowdell and Ms. Delois Maddox have also been extremely helpful to me during my stay in Auburn and I am grateful to them for all they did for me. Emmanuel Opoku and Ransford Yeboah provided research assistance during the survey data collection in Ghana and I appreciate their effort. I am also thankful to Dr. S. C. Fialor at KNUST, Ghana. Next, I gratefully acknowledge graduate assistantship from DAERS. The study was funded by the Peanut Collaborative Research Support Program/University of Georgia/Auburn University; USAID Grant no. LAG-G-00-96-90013-00. Above all, I say TO GOD BE THE GLORY for His abundant blessings. vi Table of Contents Abstract?????????????????????????????..?ii Acknowledgments????????????????????????.??iv List of Tables?????????????????...???????.???ix List of Figures??????????..????????????.?????xi Introduction?????????????????????????????1 Chapter 1: Peanut Trade and Aflatoxin Standards in Europe: Economic Effects on Exporting Countries??????????????????????...4 1.1 Background and Problem Statement???????????????4 1.2 Objectives?????????????????????????.7 1.3 Theoretical Framework????????????????????8 1.4 Conceptual Framework????????????????????9 1.5 Method???????????????????????...??13 1.5.1 Structural Model for Peanut Trade in the EU Market????????????????????......14 1.5.2 Computation of Reduced-Form Elasticities?.??????19 1.5.3 Comparative Statics????????????????.21 1.5.4 Economic Welfare Changes???????????...?..22 1.6 Data and Sources??????..???????????????..23 1.7 Results and Discussion???????????????????...25 vii 1.8 Summary and Conclusions?????????????????.?30 Chapter 2: Economic Analysis of Aflatoxin Interventions in Developing Countries: The Case of the Peanut Sector in Ghana??????????????..34 2.1 Background and Problem Statement??????????????..34 2.2 Objectives??????????????????..??????.39 2.3 Theoretical and Conceptual Framework??????..??????..39 2.4 Method and Models?????????????????????41 2.4.1 Model One: Autarky in the Peanut Market????????.42 2.4.1.1 Comparative Statics and Computation of Reduced-Form Elasticities?????????????????46 2.4.2 Model Two: Small Open Economy; Exporter with Supply Shift??????????????.????????.47 2.4.2.1 Measurement of Economic Welfare???????.52 2.4.3 Model Three: Small Open Economy; Exporter with Supply and Demand Shifts??????????????????..53 2.4.3.1 Economic Incidence Relationships from Model Three???????????????????56 2.5 Data and Sources?????????????????????...59 2.6 Results and Discussion???????????.????????.60 2.6.1 Approximated Welfare Implications: Model One versus Model Two??????????????????????..65 2.7 Summary and Concluding Remarks??????????????..66 Chapter 3: Willingness to Pay for Safer Foods: Consumer Preference for Aflatoxin- free Peanut in Ghana??????????????????????69 3.1 Problem Statement?????????????????????.69 3.2 Objectives????????????????????????...70 3.3 Related Literature?????????????????????...71 3.3.1 Importance of Food Safety to Consumers?????????..71 3.3.2 Hypothetical Bias in Contingent Valuation Studies????..?72 viii 3.3.3 Use of Double-bounded Dichotomous Choice Models??...?..73 3.3.4 Addition to the Food Safety Discussion?????????.?75 3.4 Theoretical/Conceptual Framework??????.????????..75 3.4.1 Contingent Valuation Survey in Ghana??????????.76 3.4.2 Methods of Estimating Willingness to Pay?????????80 3.4.2.1 Single-Bounded Dichotomous-Choice Method????.81 3.4.2.2 Double-Bounded Dichotomous-Choice Method???....84 3.4.2.3 Application of Semi Double-Bounded Dichotomous-Choice Method???????????????????..86 3.5 Empirical Model and Information on Variables??????..???..89 3.6 Results and Discussion???????...????????????94 3.6.1 Awareness of Aflatoxin Contamination???????...???94 3.6.2 Willingness to Pay for Aflatoxin-free Peanut????????..94 3.6.3 Factors Influencing Consumers? Willingness to Pay for Aflatoxin- free Peanut??????????????..???????..96 3.7 Summary and Conclusions?????????...????????101 References?????????????????..???????????104 Appendix 1: Additional Information for Chapter 1?????.???????..111 Appendix 2: Additional Information for Chapter 2???...?????????116 Appendix 3: Additional Information for Chapter 3????????????...118 ix List of Tables Table 1.1 Variable Information, 1995 to 2007???????????????.17 Table 1.2 Parameters and Baseline Values????????.???.?????18 Table 1.3 Price and Quantity Effects of Aflatoxin Regulation in the EU Market, 1995 to 2007??.................?????????????.?????...26 Table 1.4 Welfare Changes (US$) Induced by 10% Regulation Tax Increase in the EU Market, 1995-2007??????..????...??????...???.30 Table 2.1 Peanut Production Quantities in Ghana; 1995-2008????...????.35 Table 2.2 Average Peanut Market Information in Ghana, 1995 to 2008?????44 Table 2.3 Parameter Values??????????????????????.45 Table 2.4 Percentage Changes in Ghana?s Peanut Prices and Quantities, 1995- 2008?????????????????.???????????.61 Table 2.5 Relative Changes in Peanut Prices and Quantities, 1995-2008???...?63 Table 2.6 Welfare Changes (million US$) Induced by 10% Rise in Compliance Cost in Ghana????.????????????????????..65 Table 3.1 Selected Regions in Ghana and Sample Sizes??.?????????78 Table 3.2 Definition of Variables????????????????????.90 Table 3.3 Summary Statistics for the Continuous Variables????????...?91 Table 3.4 Summary Statistics for the Discrete Variables???????...???..93 Table 3.5 Model Estimation Results????????????...??????.98 Table 1A Peanut Import Quantity (or Market) Shares for Exporters in the EU x Market?????????????????????????...?.111 Table 1C Parameters Used to Estimate Export Supply Elasticities???.???..113 Table 1D1 Reduced-Form Elasticities for Peanut Prices and Quantities in the EU Market??????????????????????????....113 Table 1D2 Exporter Welfare Changes (US$) Induced by 10% Regulation Costs Increase????.??????????????????????.114 Table 1E Exporter Welfare Changes (1,000 US$) Induced by 10% Tax Increase: No Substitution Effects Case?????????????...??????114 Table 1F1 Price Transmission Elasticities (alpha parameters)????..????114 Table 1F2 Compliance Tax Rates (beta parameters)??????????.??114 Table 1F3 Export Quantity Share Values???????????..?????115 Table 3.5.1 Odds Ratio Estimates?????????????????.??.118 xi List of Figures Figure 1.1 Impact of Aflatoxin Tax on Peanut Prices and Quantities in the International Market????????????????????..12 Figure 2.1 Peanut Production and Distribution in Ghana from 1995-2008????.35 Figure 2.2 Incidence of Aflatoxin Tax on the Peanut Sector in Ghana???.??..46 Figure 2.3 Incidence of Peanut Aflatoxin Tax on Ghana?s Domestic and Export Markets???????????????????.??????..52 Figure 3.1 Map of Ghana Showing Distribution of Regions and Urban Centers..?..79 1 Introduction Food safety concerns continue to command growing interest from the general public, research communities, and policy-making institutions (Grunert, 2005). The attention given to food safety issues is explained by the pervasiveness of contaminants found in both domestic and global food supply chains. There are numerous contaminants cited as prominent residues that account for much of the food contamination problems around the world. Mycotoxins ?? which have been at the heart of Sanitary and Phytosanitary Standards (SPS) discussions among trading countries in recent decades ?? are some of the dominant harmful agents that perennially pose serious threats to food safety. Mycotoxins are naturally-occurring toxic substances that contaminate crops both at the pre- and post-harvest stages of food supply chains. These toxins are produced by fungi. Although many sub-groups of mycotoxins exist (such as fumonisins, zearalenone, and ochratoxins), this dissertation focuses on the most popular in recent years known as aflatoxins; due to their revealed toxicity and carcinogenicity to exposed individuals (Park et al., 2002; Jolly et al., 2006). Aflatoxins and other mycotoxins are noted for contaminating over one-quarter of food supply worldwide (Dohlman, 2003), causing enormous revenue losses in food trade (Otsuki, Wilson, and Sewadeh, 2001), as well as inducing severe illnesses/mortalities in humans and farm animals (Williams et al., 2004; Liu and Wu, 2010). Food crops that are often cited as susceptible to aflatoxin contamination include cereal grains, nuts, vegetables and fruits. This dissertation focuses on the aflatoxin contamination problem in peanut ?? in line with goals of the USAID Peanut Collaborative Research Support Program ?? though the issues discussed are fundamentally similar to many 2 vulnerable food crop industries. Aflatoxin contamination occurs in many food chains around the world, particularly in Africa and Asia (Wang et al., 2001; Dash et al., 2007). Environmental conditions in warm regions are known to present challenges to eliminating aflatoxins entirely (Dohlman, 2003). Therefore, the introduction and enforcement of aflatoxin standards (i.e. setting permissible thresholds) for food have been the natural policy interventions from governments and international regulators to control the problem. Public health interests primarily drive the need to minimize dietary aflatoxin exposure in order to protect consumers. A host of nations including the European Union (EU) and the United States (US) are implementing own aflatoxin regulations for peanut ?? 4 ppb and 20 ppb, respectively ?? aimed at safeguarding the health of the consuming public (Otsuki, Wilson, and Sewadeh, 2001). In the near future, as the world gets increasingly integrated, it is anticipated that most countries will converge in enforcing uniform food standards. It is, therefore, necessary to study the aflatoxin problem and show possible policy implications of regulations on important food industries. Against this background, the dissertation comprises of three chapters that explore central topics representing the following broad research objective: determining the economic implications of aflatoxin regulations on peanut market participants. Specifically, Chapter One focuses on international trade in peanut by studying the impact of European aflatoxin standards on prices faced by exporting countries on one hand, and consumers in Europe on the other. The economic welfare implications are discussed. In Chapter Two, the analysis in Chapter One is extended for a specific-country 3 case; where I evaluate economic incidence owing to the introduction of aflatoxin regulations to the peanut sector in Ghana. Here, economic losses and gains derived from aflatoxin regulation enforcement are explored through price and quantity effects on market participants. The importance of trade status as well as consumer preferences are highlighted with respect to the distribution of policy impacts among suppliers and consumers. Chapter Three complements the preceding two topics by studying consumer preferences for aflatoxin-free peanut. This goal is pursued with a focus on Ghana by analyzing consumers? willingness to pay for peanut with reduced aflatoxin levels. The final chapter offers completeness to the broad research objective in that it would be possible to empirically understand consumer valuation of safer peanut. Knowledge of consumer preference for aflatoxin-free peanut will enable policymakers to properly interpret whether inevitable increases in retail prices (shown in the preceding chapters) are welfare decreasing or not. In what follows, detailed discussions on each of the three dissertation chapters are provided. 4 Chapter 1: Peanut Trade and Aflatoxin Standards in Europe: Economic Effects on Exporting Countries 1.1 Background and Problem Statement Mycotoxins are one of the broad groups of food contaminants that receive close attention in international trade.1 One of the most popular types of mycotoxins is aflatoxin, known to be carcinogenic and immunosuppressive (Wild and Hall, 1999; Williams et al., 2004; Dash et al., 2007). 2 The adverse health impacts of mycotoxins and other food residues are the reasons for growing food safety concerns among researchers and policy makers. In recognition of the potential health risks posed by aflatoxins, the World Trade Organization (WTO) has an agreement on Sanitary and Phytosanitary Standards (SPS) that allows members to set their own standards for food when necessary to protect consumers (Yue, Beghin and Jensen, 2006). The WTO refers to this SPS policy as the ?precautionary principle?. Since 1998, food standards in industrialized countries have evolved with the European Commission announcing new aflatoxin regulations for imported food items (Otsuki, Wilson and Sewadeh, 1 Mycotoxins - one of the most prominent food contaminants - are composed of toxic chemical substances produced by fungi which contaminate crops during production and or in post-harvest handling. 2 The major aflatoxins of concern are designated B1, B2, G1 and G2. These aflatoxins are usually found together in contaminated food items with B1 being the most toxic and accounting for 50-70% of total aflatoxins level. These compounds are regarded as carcinogenic food contaminants whose consumption should be reduced to ?reasonably acceptable? levels (see FAO-WHO 1997; Otsuki, Wilson and Sewadeh, 2001a, 2001b). 5 2001a, 2001b).3 However, setting appropriate aflatoxin levels for food crops, especially peanut and peanut products, has been a controversial subject that has generated much interest among trading partners. For instance, the European Union (EU) aflatoxin standards require that peanuts for direct human consumption must contain levels not exceeding 4ppb. Interestingly, the joint FAO/WHO Codex Alimentarius Commission (Codex) which has the mandate of setting international food standards recommends an acceptable aflatoxin level of 15 ppb, while the United States of America (USA) accepts 20 ppb for all peanut products. Clearly, the EU has one of the strictest food regulations in the world market, and peanut is one of the most affected food crops whenever aflatoxin standards are tightened.4 The EU together with Canada, Japan, and Mexico consume over 60% of world peanut trade; Europe alone imports about 40% of trade in peanut (FAOSTAT). Limited peanut production (less than 1.0% of consumption) explains Europe?s heavy reliance on imports. The major peanut exporters to the EU market are China, USA, Latin America, and Africa. Boonsaeng, Fletcher, and Carpio (2008) indicate that Argentina, China, and the USA accounted for 70% of the world peanut exports in 2005. In terms of global peanut production (i.e. sum of domestic and export supply), developing countries produce over 60% of the crop (Upadhyaya et al., 2003). Imported peanuts are consumed directly and/or further processed into snacks, butter, candies, chocolate bars, among other peanut products. 3 According to Otsuki, Wilson and Sewadeh (2001b), EU members had country-specific aflatoxin standards until 1998 after which the EC fixed total aflatoxin levels for processed peanut at 15 ppb (8 ppb for B1) and in other nuts and dried fruits subject to further processing at 10 ppb (5 ppb for B1). Furthermore, cereals, dried fruits and nuts for direct human consumption were set at 4ppb (2 ppb for B1). These harmonized regulations were enacted and implemented by all EU member countries since 2002. 4 Peanut (also called groundnut) is one of the world's most popular crops (Nwokolo, 1996). 6 Following increasing concerns over possible deleterious influences of standards on trade flow, a number of studies have emerged. Notable ones include Otsuki, Wilson, and Sewadeh (2001a, 2001b); Yue, Beghin, and Jensen (2006); Nogueira et al. (2008); and Nguyen, and Wilson (2009). Particularly, Otsuki, Wilson, and Sewadeh studied the impact of EU aflatoxin standards against that of Codex on Africa?s food exports by using the gravity model. The authors found that African food exports to Europe are adversely affected by stringent aflatoxin standards. Acknowledging one major limitation of using gravity models to assess the impact of regulations on trade, Otsuki, Wilson, and Sewadeh (2001a, p. 272) state that ?as a result of the structure of a gravity model, the separate effects of ? standards on import demand and export supply cannot be isolated.? Almost all existing studies that employ gravity models show negative SPS impact on trade. An exception is Xiong and Beghin (2010) whose follow up study ?? with reference to forecasts by Otsuki, Wilson and Sewadeh concerning impacts of EU aflatoxin standards on African peanut exports ?? yield no substantial trade quantity effect. Even though Xiong and Beghin also employed the gravity model, they improved on two limitations identified in Otsuki, Wilson and Sewadeh?s work; namely the use of time-invariant aflatoxin- contamination data for the entire study period, as well as possible sample selection bias problem due to exclusion of zero trade records in the analysis. Xiong and Beghin (2010) argue that EU standards have no significant effects on Africa?s peanut trade volumes because Africa has its own domestic barriers that undermine export trade. In spite of the cited studies, literature on economic impacts of SPS on food exports is still limited as noted in Otsuki, Wilson and Sewadeh (2001b); and Maskus and Wilson (2001). Moreover, a sizeable proportion of research work regarding effects of food standards is descriptive with only a few quantitative studies (Josling 7 and Roberts 2011) mostly employing the gravity model and its variants in assessing the impact of aflatoxin standards on trade flows. Furthermore, there is consensus among researchers on the lack of studies focusing on precise economic welfare effects of regulations (Otsuki, Wilson and Sewadeh, 2001b; Wilson, 2003; Roberts, 2009; and Xiong and Beghin, 2010); hence more research attention on economic effects is needed. This chapter, therefore, contributes to the literature by determining price and quantity effects and, by implication, economic welfare impacts of EU aflatoxin regulation tightening on edible peanut trade. To this end, the equilibrium displacement modeling technique is employed to evaluate the economic incidence of EU aflatoxin standards on consumers and trade partners.5 1.2 Objectives The principal objective of this study is to determine the effects of EU aflatoxin regulation tightening on prices received by peanut exporters to the European market, as well as consumer prices. This study focuses on trade in edible peanuts, both shelled and in-shell.6 Peanut suppliers of interest are China, USA, and rest of the world (ROW) exporters.7 ROW is composed of exporting countries from Latin America, Asia, and Africa. 5 Henceforth, aflatoxin standards/regulations are equally referred to as standards or regulation 6 The terms ?peanut? and ?edible peanut? are used interchangeably. 7 China, USA and Argentina account for the bulk of edible peanut exports (Diop, Beghin and Sewadeh, 2004). 8 1.3 Theoretical Framework Classic theories of tax incidence and economic equilibrium provide the structure for analyses in this study. Concerns about the distribution of tax burdens in affected systems ?? referred to as shiftability of tax ?? have motivated an extensive tax incidence literature following Ricardo?s insights (Kittrel, 1957; Kotlikoff and Summers, 1987). The literature distinguishes between statutory burden (incidence) and economic burden of taxes. Statutory incidence refers to governments? distribution of obligatory tax payments among economic agents, whereas economic incidence determines the impact of such taxes on important equilibrium variables such as prices in a system (Kotlikoff and Summers, 1987; Metcalf, 2006). Tax incidence analysts, therefore, study the distribution of tax implications on the welfare of agents in a given system; appropriately focusing on economic incidence irrespective of which side of the market directly paying the tax. Generally, Kotlikoff and Summers (1987) show that the theory of economic equilibrium is invoked to analyze the effects of exogenous interventions, such as tax policies, on equilibrium prices in a system. Thus, tax incidence analysts assume perfectly competitive partial or general equilibrium frameworks to understand the distribution of tax burdens among market participants. Two important principles that have been established from economic analyses of tax incidence are as follows: (1) economic incidence of tax policies is absolutely insensitive to which side of the market the tax is imposed upon; and (2) greater share of any tax burden is borne by the less elastic side of the market, whether the supply or the demand side (Kotlikoff and Summers, 1987; Metcalf, 2006). In other words, statutory tax incidence is immaterial in economic analyses in that the absolute magnitudes of supply and demand elasticities determine the distributional impact of tax interventions on the welfare of economic agents. 9 In addition, analyses in this dissertation are primarily based on discussions in the trade literature concerning the quantification of non-tariff (or technical) trade barriers (Maskus and Wilson, 2001; Beghin, 2006). Thus, SPS policies are characterized as traditional tariff barriers in the assessment of economic impacts. The theoretical foundations introduced in this section are conceptualized in the next section. 1.4 Conceptual Framework This chapter applies the tariff equivalent method to quantifying SPS compliance costs using a price-wedge approach (Calvin and Krissoff, 1998; and Yue, Beghin and Jensen, 2006). Therefore, EU aflatoxin regulation for international trade in peanut is modeled as a tax. The theories of economic equilibrium and tax incidence are characterized below through an application of the equilibrium displacement modeling (EDM) policy evaluation tool; to determine the distribution of EU aflatoxin tax burden on peanut trade. A partial equilibrium setting is used given that the size of the peanut sector in most producing countries is small relative to their economies. In line with the EDM literature, the following assumptions are made about the peanut trade market: (1) Perfect competition exists in the market; (2) Market clears; (3) Demand and supply curves shift in parallel fashion following the exogenous intervention (or shock) on equilibrium, as a result of aflatoxin-compliance tax. Sources in the literature that have worked with the preceding assumptions are Alston, Norton and Pardey, 1995, p. 60-63; Wohlgenant, 1999; and Sun and Kinnucan, 2001, among others. 10 Furthermore, this study operationalizes the effect of EU aflatoxin standards as an import tax since exporters? efforts to comply with regulations often introduce additional costs (Maskus and Wilson, 2001) into the international peanut supply chain. Aflatoxin contamination can occur at any point along the supply chain. Hence, peanut importers in Europe also face the aflatoxin tax although this is irrelevant to the ultimate distribution of economic burden from the tax. Aflatoxin compliance costs collectively refer to additional expenses (and losses) incurred by peanut suppliers and importers as they attempt to conform to strict aflatoxin standards, both at the border and within EU countries, in order to sell to consumers.8 Thus, compliance costs encapsulate utilization of proper storage environments; inspection, sampling, testing and certification costs; storage and handling costs of rejected supplies; discarded loads of contaminated peanut, costs incurred as a result of transportation to alternative and often less-attractive markets; as well as relatively low prices received from alternative uses of the rejected product such as feed, biofuels, among others. Therefore, an increase in aflatoxin regulation implies tighter standards leading to higher compliance costs which have economic welfare impacts identical to conventional tax incidence. In addition, this study models the EU aflatoxin compliance ?tax? as an import demand shifter, taking advantage of the principle that economic incidence of a tax is irrespective of statutory incidence. Precisely, the import demand curve shifts inward thereby lowering export supply price and raising EU import demand price. The import tax is assumed to constitute a major component of the wedge between peanut export and import prices.9 8 See Shafaeddin (2007) for more on SPS compliance costs. Also, to understand the broad nature of mycotoxin control strategies, see Dohlman (2003) and Codex Alimentarius Commission (2003). 9 Export prices used here are actually peanut producer prices because the FAOSTAT data report export and import unit prices as exactly identical. Thus, using producer prices helps to approximate aflatoxin 11 Figure 1.1 is a pictorial demonstration of the economic incidence on peanut market agents after introduction of the EU aflatoxin tax. As depicted in the graphical analyses, it is expected that the regulation tax, R, would be shared between the two sides of the market. Specifically, the magnitude of the tax is composed of ?a? and ?b?, respectively borne by consumers and exporters. Thus, the consumer price is ultimately increased from P0 to PD, while the final price that goes to suppliers is depressed from P0 to PS. These price effects translate into losses in economic welfare on both sides of the peanut market. Before imposition of the tax, the economic welfare of exporters is measured as equivalent to the area of triangle P0e0g which reduces to the area of triangle PSe1g after the tax. Also, consumers face a decrease in economic surplus since the tax policy reduces their welfare from the area of triangle P0e0n to area of triangle PDln. compliance costs. 12 Figure 1.1. Impact of Aflatoxin Tax on Peanut Prices and Quantities in the International Market Following applications of EDM, structural elasticities are obtained from previous studies. The elasticities are then used in the model to generate reduced-form elasticities after shocking initial equilibrium with regulation tax. The resulting reduced-form elasticities reflect how export and import quantities and corresponding prices are impacted by a 1% change in compliance costs associated with aflatoxin regulation tightening. Also, simulations are performed to understand approximate levels of exporter and consumer welfare changes triggered by a 10% regulation tightening in Europe. The EU?s decision to harmonize its regulation engendered drastic strictness in permissible aflatoxin levels in most member countries. For example, major importing countries, namely the Netherlands, Italy, Spain and Sweden tightened permissible aflatoxin levels to about 50% including Belgium, Greece, Ireland and Lexumbourg (Otsuki, Wilson and Sewadeh, 2001b). P Q P0 Q0 D0 S Q1 PD R D1 PS e0 e1 l n u f g a b 13 Hence, those changes provide the basis for examining economic welfare implications of a 10% regulation tightening. Typically, compliance costs data are not readily available (Henson et al., 2000). The scarcity of data drives the use of tariff equivalent rates of standards as indirect measures of compliance costs. Basically, tariff equivalent approaches are based on the price differential between domestic and import prices of a homogeneous commodity. That is, the tariff equivalent idea asserts that the effects of SPS can be approximated after separating tariffs, transportation and other transaction costs from the price gap (Calvin and Krissoff, 1998; and Yue, Beghin and Jensen, 2006). By virtue of the unavailability of direct data on aflatoxin compliance costs, this study applies a price-wedge approach to estimating compliance costs similar in principle to the tariff equivalent method (see the model section below). According to Beghin and Bureau (2001), ?the price wedge measures the difference between the internal price of a good and the reference price of a comparable good, such as a border price. It attributes the price difference to trade barriers and transportation cost. The price wedge can be expressed as per unit (specific) tax/tariff, or ad valorem tax/tariff.? 1.5 Method A widely acknowledged limitation of the gravity model is its failure to isolate effects of regulations on supply and demand relations. Thus, studying the implications of food standards using gravity models, it is impossible to delineate welfare impacts on suppliers and consumers (Otsuki, Wilson and Sewadeh, 2001b; Wilson, 2003). The dissertation, therefore, addresses this gap in the literature by introducing the Equilibrium Displacement Modeling framework, grounded in the theory of tax 14 incidence.10 Piggot (1992) highlighted the importance of using the EDM technique as a tool for agricultural policy analysis. Consequently, influential studies such as Wohlgenant (1993); Davis and Espinoza (1998); Sun and Kinnucan (2001); Kinnucan and Myrland (2002, 2005); and Mutondo, Brorsen, and Henneberry (2009) have applied the EDM approach in analyzing various agricultural policies. In this chapter, international trade in the EU market is characterized below through an application of the EDM method. As consignments of peanut move along the supply chain, costs related to compliance with EU aflatoxin standards are modeled as tax introduced into the peanut industry. 1.5.1 Structural Model for Peanut Trade in the EU Market The structural model representing the EU market assumes that edible peanuts are differentiated by country of origin as shown in Boonsaeng, Fletcher, and Carpio (2008). Given the evidence that edible peanuts are source-differentiated, separate supply equations are specified for each exporting country and demand interrelationships included on the demand side. This chapter assumes that regulation tightening ?? which translates into increases in compliance costs (or tax) ?? is a demand shifter affecting only costs but not consumer preference for quality. In other words, this chapter ignores any quality-improvement effects of the aflatoxin standards and focuses on costs of compliance. Initial equilibrium in the peanut trade market is represented by the following structural model: 10 Wohlgenant (2011) provides a good review on EDM. 15 g4666uni0031g4667g3398g4666uni0033g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1839g3036 g3404g1839g3036g4666g1842g2869g3005uni002Cg1842g2870g3005uni0009uni002Cg1842g2871g3005g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1861g1865g1868g1867g1870g1872uni0009g1856g1857g1865g1853g1866g1856g4669 g4666uni0034g4667g3398g4666uni0036g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3036g3005 g3404uni0009g1842g3036g3020 g3397g1846g3036 g3397g1829g3036uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1868g1870g1861g1855g1857uni0009g1864g1861g1866g1863g1853g1859g1857g4669 g4666uni0037g4667g3398g4666uni0039g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1850g3036 g3404g1850g3036uni0009g3435g1842g3036g3020g3439uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1857g1876g1868g1867g1870g1872uni0009g1871g1873g1868g1868g1864g1877g4669 g4666uni0031uni0030g4667g3398g4666uni0031uni0032g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1850g3036 g3404uni0009g1839g3036 g3404g1843g3036uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1865g1853g1870g1863g1857g1872uni0009g1855g1864g1857g1853g1870g1861g1866g1859g4669 where Mi denotes quantity of EU import demand from an exporting country, Xi represents quantity of peanut supplied by different exporters to the EU market, PiD is import demand price for peanut from different origins, PiS is export price received by a particular peanut supplier, Ti is per-unit transportation cost, and Ci is per-unit compliance cost or ?tax? that captures aflatoxin regulation tightening. The superscripts D and S denote demand and supply, respectively. Subscript i= 1, 2, and 3; representing China, USA and ROW, respectively. Values for some of the variables have been provided below in Table 1.1. Endogenous variables in the model are Mi, Xi, PiD, and PiS while Ti and Ci are exogenous. It is worth emphasizing that the compliance ?tax? variable is the exogenous variable of primary interest albeit there are several others that have been ignored for the purposes of this study. Equations (1) - (3) capture EU import demand with respect to each of the exporting countries under consideration. These equations state that EU import demand for a given trading partner is determined by the demand price for that particular supplying country, and demand prices for the other suppliers. The price-linkage equations (i.e. (4)-(6)) account for the relationship between country-specific export prices and import demand prices prevailing in the EU market. Owing to peanut trade liberalization policies (Beghin, Diop and Matthey, 2006), the wedge between export supply and import demand prices is assumed to comprise of 16 standards compliance costs plus costs of transporting peanut to the EU market.11 These price equations provide the necessary links to modeling the regulation as tax and ultimately distinguishing import price effects from export price effects. In other words, the price-wedge approach is critical to understanding the distributional impact of the aflatoxin regulation policy. Furthermore, source-specific peanut export supply is assumed to be influenced by the exporter?s supply price and this has been accounted for in equations (7)-(9). Export supply prices exclude all costs required to transfer peanuts from the supplying country to the EU market, as well as the cost of complying with aflatoxin standards. Finally, the market clearing conditions, equations (10)-(12), ensure that the system is closed. Precisely, in equilibrium import demand quantities for each exporter are exactly identical to their corresponding export supply quantities. 11 Transportation costs have been suppressed in this model since the main focus is on compliance costs induced by regulation tightening. The basic price equations are D Si i i ip p t c= + + where ti is the per- unit transfer costs, and ci is the per-unit compliance cost or ?tax.? After suppressing transfer costs, these equations are written in percentage changes as in equations (4?)-(6?) below. 17 Table 1.1. Variable Information, 1995 to 2007 Variable Definition Value 1995- 2007 1995- 1998 1999- 2002 2003- 2007 Mchina Import quantity from China (MTa) 167804 133458 187697 179367 Musa Import quantity from USA (MT) 107071 147957 114428 68476 Mrow Import quantity from ROW (MT) 365316 352792 296165 430656 PDchina EU demand price for China?s peanut (US$ /MT) 799 845 670 865 PDusa EU demand price for USA?s peanut (US$/MT) 1035 975 1031 1084 PDrow EU demand price for ROW?s peanut (US$/MT) 796 795 1119 771 PSchina Export price for China?s peanut (US$/MT) 460 464 395 508 PSusa Export price for USA?s peanut (US$/MT) 514 634 520 413 PSrow Export price for ROW?s peanut (US$/MT) 385 399 312 433 Source: Computed from FAO Statistics (2010). Note: aMT denotes ?metric tonnes? and import demand quantities are identical to corresponding export supply quantities. We proceed by expressing the structural model in percentage changes (displaced form) as follows: g4666uni0031g4593g4667g3398g4666uni0033g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1839g3036uni2217 g3404g3533g2015g3036g3037g1842g3037g3005uni2217 g2871 g3037g2880g2869 uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1861g1865g1868g1867g1870g1872uni0009g1856g1857g1865g1853g1866g1856g4669 g4666uni0034uni2032g4667g3398g4666uni0036g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3036g3005uni2217 g3404uni0009g2009g3036g1842g3036g3020uni2217 g3397uni0009g2010g3036g1844uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1868g1870g1861g1855g1857uni0009g1864g1861g1866g1863g1853g1859g1857g4669 g4666uni0037uni2032g4667g3398g4666uni0039g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1850g3036uni2217uni0009g3404g2013g3036g1842g3036g3020uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1857g1876g1868g1867g1870g1872uni0009g1871g1873g1868g1868g1864g1877g4669 g4666uni0031uni0030g4593g4667g3398g4666uni0031uni0032g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009g1850g3036uni2217 g3404uni0009g1839g3036uni2217 g3404uni0009g1843g3036uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1865g1853g1870g1863g1857g1872uni0009g1855g1864g1857g1853g1870g1861g1866g1859g4669 All parameters in the displaced model are defined in Table 1.2 together with corresponding values. Asterisks indicate percentage changes in the respective variables; for instance, g1850g3036uni2217uni0009g3404 g3031g3025g3284g3025 g3284 . In all, the displaced model consists of 12 endogenous variables (g1842g3036g3020uni2217, g1842g3036g3005uni2217, g1850g3036uni2217 and g1839g3036uni2217 uni0077uni0068uni0065uni0072uni0065uni0009g1861 g3404g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875), and a single exogenous variable (g1844uni2217). 18 The exogenous variable, g1844uni2217, is the uniform percentage increase in standards (or compliance costs); translated by its coefficients as compliance tax (or standards) elasticities. The form of the model in equations (1?)-(12?) is referred to as equilibrium displacement model; facilitating the derivation of reduced-form elasticities (Kinnucan and Myrland, 2002; Wohlgenant, 2011). Empirical values for structural elasticities and parameters in the model are required to derive the needed reduced-form elasticities. Sources and rationale behind all parameter values selected for simulations in this chapter are later discussed in the data section. Table 1.2. Parameters and Baseline Values Parametera Definition Valueb g275111 Own-price import demand elasticity; China -1.743 g275122 Own-price import demand elasticity; USA -1.868 g275133 Own-price import demand elasticity; ROW -0.275 g275112 Cross-price import demand elasticity; China?s quantity and USA?s price 0.703 g275121 Cross-price import demand elasticity; USA?s quantity and China?s price 0.893 g275113 Cross-price import demand elasticity; China?s quantity and ROW?s price 0.074 g275131 Cross-price import demand elasticity; ROW?s quantity and China?s price 0.678 g275123 Cross-price import demand elasticity; USA?s quantity and ROW?s price -0.441 g275132 Cross-price import demand elasticity; ROW?s quantity and USA?s price -0.591 g20091 Price transmission elasticity for China 0.576 g20092 Price transmission elasticity for USA 0.497 g20093 Price transmission elasticity for ROW 0.484 g27461 Compliance tax rate for Chinac 0.111 g27462 Compliance tax rate for USAc 0.419 g27463 Compliance tax rate for ROWc 0.202 g22391 China?s peanut export supply elasticityd 18.766 g22392 USA?s peanut export supply elasticityd 4.625 g22393 ROW?s peanut export supply elasticityd 10.8 Notes: aSubscripts 1, 2 and 3 refer to China, USA, and ROW, respectively. bDemand elasticities come from Boonsaeng, Fletcher, and Carpio (2008) and the remaining parameter values are computed from FAO Statistics (2010) data. cSee Appendix 1 for explanation on computation. dSee Appendix 1 for details. Also, Kinnucan and Myrland (2008) provide more information on computing theoretically-consistent export supply elasticities. 19 1.5.2 Computation of Reduced-Form Elasticities Following Kinnucan and Myrland (2002, 2005), I solve the displaced model using matrix algebra to obtain the reduced-form elasticities ?? with the aid of Microsoft Excel spreadsheet. To clearly see the direct effect of standards tightening on demand quantities, the price equations (4?)-(6?) are plugged into (1?)-(3?) before proceeding to express the displaced model in matrix form. Performing the preceding substitution yields the tax-burdened import demand equations (13)-(15) as follows: g4666uni0031uni0033g4667g3398g4666uni0031uni0035g4667uni0009uni0009g1839g3036uni2217 g3404g3533g2009g3037g2015g3036g3037g1842g3037g3020uni2217 g3397uni0009g2038g3036g1844uni2217uni0009 g2871 g3037g2880g2869 uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1872g1853g1876g3398g1854g1873g1870g1856g1857g1866g1857g1856uni0009g1861g1865g1868g1867g1870g1872uni0009g1856g1857g1865g1853g1866g1856g4669 where g2038g3036 g3404uni2211 g2010g3037g2015g3036g3037uni0009g2871g3037g2880g2869 is the composite standards elasticity that takes into account demand interrelationships among the various origins of peanut export supply. However, when the assumption that peanuts are source-differentiated is relaxed, the tax-burdened import demand equations (13)- (15) reduce to equations (16)- (18) below: g4666uni0031uni0036g4667g3398g4666uni0031uni0038g4667uni0009uni0009uni0009uni0009uni0009g1839g3036uni2217 g3404uni0009g2009g3036g2015g3036g3036g1842g3036g3020uni2217 g3397g2010g3036g2015g3036g3036g1844uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 The latter import demand equations suggest that in the absence of demand interrelationships, one should expect an increase in R (i.e., regulation tightening) to cause a reduction in the quantity of peanut imported into Europe. We expect the foregoing result since price transmission elasticity (?i) and compliance tax rate (?i) are positive in sign, while own-price import demand elasticity (?ii) is negative. Also, the preceding logic indicates that tightening the regulation lowers export prices whereas equations (4?)-(6?) show that import demand prices rise (see comparative statics 20 below for more)12. However, maintaining the assumption that substitution effects exist among peanut from different sources (i.e. presence of demand interrelationships) leaves the composite standard elasticity (g2038g3036) with an indeterminate sign. The implication is that the nature and strength of substitution effects (i.e. cross-price elasticities) exert mixed impacts on exporter prices in that there is the possibility of some exporting countries actually benefiting from standards tightening while others suffer losses. It is, therefore, important to compare results from the two model scenarios i.e. with and without demand interrelationships. Next, the displaced model is expressed in matrix form as follows: g4666uni0031uni0039g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g2224g2181g3404g2211g2182uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g2734 is a 9 x 9 matrix of endogenous-variable coefficients (or parameters), Y is a 9 x 1 vector containing endogenous variables, g2721 is a 9 x 1 vector of exogenous- variable coefficients, and Z is a 1 x 1 vector containing the only exogenous variable in the model. Equation (19) is then pre-multiplied by the inverse of g2734uni0009which yields the following reduced-form equation:uni0009 uni0009g4666uni0032uni0030g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g2181g3404g2161g2182uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g2161g3404uni0009g2224g2879g2869g2211 is a 9 x 1 vector whose elements are the reduced-form elasticities. Notice that the number of endogenous variables is nine instead of twelve since the price-linkage equations were earlier substituted into the demand equations before carrying out the matrix algebra. Consequently, I derive the regulations effect on demand prices by plugging the appropriate percentage changes for export prices into the price-linkage equations, (4?)-(6?). 12 To clearly see effect of regulation tightening on export prices, we solve for g1842 g3036 g3020uni2217 in equations (16) ? (18) which gives: g1842g3036g3020uni2217 g3404uni0009 uni0031g2009 g1861g2015g1861g1861 uni0009g1839g3036uni2217 g3398g2010g1861g2009 g1861 g1844uni2217uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009uni0009uni0009g1861 g3404uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875. 21 1.5.3 Comparative Statics In what follows, analytical solutions for reduced-form elasticities are provided to highlight how the basic model works by deriving incidence relationships for the case where demand interrelationships are ignored. We proceed by substituting the demand equations (16)-(18) together with the supply equations (7?)-(9?) into the equilibrium equations (10?)-(12?) yielding: g4666uni0032uni0031g4667g3398g4666uni0032uni0033g4667uni0009uni0009uni0009g2013g3036g1842g3036g3020uni2217 g3404uni0009g2009g3036g2015g3036g3036g1842g3036g3020uni2217 g3397g2010g3036g2015g3036g3036g1844uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1861 g3404uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 From equations (21)-(23), we solve for reduced-form elasticities regarding export supply prices as follows: uni0009g4666uni0032uni0034g4667g3398g4666uni0032uni0036g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3036 g3020uni2217 g1844uni2217 g3404uni0009 g2010g3036g2015g3036g3036 g2013g3036g3398g2009g3036g2015g3036g3036uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1861 g3404uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Next, we derive effects of regulation tightening on import demand prices by plugging the above export price effects into the price equations (4?)-(6?), resulting in the following price impact: g4666uni0032uni0037g4667g3398g4666uni0032uni0039g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3036 g3005uni2217 g1844uni2217 g3404uni0009 g2010g3036g2013g3036 g2013g3036g3398g2009g3036g2015g3036g3036uni0009g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1861 g3404uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Similarly, to obtain corresponding demand and supply quantity effects, we substitute the export price effects into either the export or import quantity equations above. Specifically, plugging equations (24)-(26) into (7?)-(9?) yields: g4666uni0033uni0030g4667g3398g4666uni0033uni0032g4667uni0009uni0009uni0009uni0009g1850g3036 uni2217 g1844uni2217 g3404uni0009 g2010g3036g2015g3036g3036g2013g3036 g2013g3036g3398g2009g3036g2015g3036g3036uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1861 g3404uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Hence, from the market clearing conditions, g1850g3036uni2217 g1844uni2217 g3404uni0009 g1839g3036uni2217 g1844uni2217 g3404uni0009 g1843g3036uni2217 g1844uni2217uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1861 g3404uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875 These comparative statics solutions (reduced-form elasticities) show the incidence relations of standards tightening on exporters and EU consumers. 22 The results suggest that the tightening of aflatoxin regulation reduces export prices received by suppliers and increases import prices faced by EU consumers, as illustrated earlier in Figure 1.1. In addition, the economics principle that the less elastic side of a market bears the greater incidence of policy interventions can be shown clearly. Stated differently, the less elastic side of the peanut market (i.e. the demand side since absolute values of demand elasticities are consistently less than supply elasticities in Table 1.2) is expected to bear the greater economic incidence of aflatoxin regulation tightening. For example, if export supply is perfectly elastic then equations (24)-(26) reduces to uni0009uni0009uni0009g3017g3284 g3268uni2217 g3019uni2217 g3404uni0009uni0030, while equations (27)-(29) become uni0009uni0009uni0009g3017g3284g3253uni2217 g3019uni2217 g3404uni0009g2010g3036uni0009; implying that the entire incidence of standards tightening would be borne by EU consumers. 1.5.4 Economic Welfare Changes The estimated reduced-form elasticities are used in welfare measurement formulas derived from Figure 1.1 (see Wohlgenant, 1993, 1999; Alston, Norton and Pardey, 1995; Sun and Kinnucan, 2001; Mutondo, Brorsen and Henneberry, 2009). With the regulation tax intervention as demand shifter, the appropriate formulas for approximating producer and consumer economic surplus changes are stated in equations (33) through (38). In Figure 1.1 above, it is instructive to note that change in producer welfare equals the difference between the areas delineated by triangles P0e0g and PSe1g. Similarly, the change in consumer welfare is approximated by subtracting the area of triangle P0e0n from that of triangle PDln. Thus, both sides of the market are expected to experience losses in economic welfare, following 23 imposition of the regulation tax. For each side of the market, economic surplus before the tax intervention exceeds the surplus after the policy. g4666uni0033uni0033g4667g3398g4666uni0033uni0035g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1986g1842g1845g3036 g3404g1842g3036g3020g1843g3036g1842g3036g3020uni2217uni0009g4666uni0031g3397uni0030uni002Euni0035g1843g3036uni2217uni0009g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1861 g3404uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875 g4666uni0033uni0036g4667g3398g4666uni0033uni0038g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1986g1829g1845g3036 g3404g4666g1848g3036g3005 g3398g1842g3036g3005uni2217uni0009g4667g1842g3036g3005g1843g3036g4666uni0031g3397uni0030uni002Euni0035g1843g3036uni2217uni0009g4667uni0009uni0009uni0009uni0009g1861 g3404uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875 where g1986g1842g1845g3036 is change in welfare (surplus) for a given exporter; uni0009g1986g1829g1845g3036 represents change in consumer welfare regarding demand for a given exporter?s peanut; g1842g3036g3020 is supply price received by a given exporter in the initial equilibrium; g1842g3036g3005is the initial equilibrium demand price faced by EU consumers for an exporter?s product; g1843g3036 is quantity traded in the initial equilibrium; g1842g3036g3020uni2217 and g1842g3036g3005uni2217are as defined earlier; and g1848g3036g3005is the vertical shift in a particular demand as a result of regulation tightening. In the model scenario where demand interrelationships are ignored, g1848g3036g3005 g3404uni0009 g3081g3284g2879g3080 g3284 g1844uni2217uni0009g3407uni0030; where the negative sign indicates a downward (inward) demand shift in response to regulation tightening (see Appendix 1 for more on the derivation of g1848g3036g3005).13 1.6 Data and Sources Peanut exporting countries covered in this chapter are China, USA, and ROW. According to Boonsaeng, Fletcher, and Carpio (2008), in-shell peanut exports from China and USA form over three-quarters of edible peanut imported into the EU (see Appendix 1A for details on import quantity shares). 13 For details on how to correctly compute economic welfare changes when products are related in consumption (for example, existence of substitution effects in demand as in this study), see Alston, Norton and Pardey (1995, pp. 237-245). 24 Other prominent peanut exporting countries to the EU market, namely Argentina, India, Brazil, Vietnam and some African countries (Egypt, South Africa, Senegal, Sudan, Malawi and Gambia) have been aggregated as ROW.14 The EU data comprise aggregation of all member countries in the relevant period of this study.15 A panel of annual trade value and quantities for edible peanut was obtained from FAOSTAT (2010) database. Unit prices were derived from the trade value and quantity data. Boonsaeng, Fletcher and Carpio (2008) analyzed EU import demand for in- shell peanuts from USA, China, and ROW. In the literature, the aforementioned work is the only study to have estimated EU peanut import demand hence their price elasticity values are employed in this chapter.16 Boonsaeng, Fletcher and Carpio (2008) found peanut from China and USA to be highly substitutable. Similarly, cross- price elasticities suggest that Chinese and ROW peanut are gross substitutes. Conversely, USA and ROW peanut were found to be gross complements (see Table 1.2 for the demand elasticity values). 14 Average prices and transportation costs for Argentina and India are used to represent ROW except for export and import quantities where all countries are used in the computation. Moreover, exporters that make up ?ROW? in the current paper may not be exactly identical to that of Boonsaeng, Fletcher, and Carpio (2008) but may be close since the aggregate comprises Latin American and African countries. 15 The peanut demand quantities used for this paper cover the entire 27 EU member countries over the period 1995 through 2007. Average of import prices offered in the United Kingdom, the Netherlands and France markets are used to represent the EU due to the prominence of these importing countries in the European peanut market. 16 Although Boonsaeng, Fletcher and Carpio?s study focused on edible in-shell peanuts only, the current paper assumes that their elasticities can be generalized for all edible peanuts (i.e. both shelled and in-shell peanuts). 25 Regarding peanut export supply elasticities, no estimates are available in the literature. However, Kinnucan and Myrland (2008), provide a theoretically-consistent formula for approximating export supply elasticities. Hence, I apply the formula in this chapter (see Appendix 1.C for formulas and details on how export supply elasticities are computed in the present study). As evident in Table 1.2 above, supply elasticity values are all elastic given that peanut exports in each supplying country account for less than 15% of domestic production (see Appendix 1A for export quantity shares). The last set of parameter values ?? compliance tax and price transmission elasticities ?? are computed using data from the following sources: (1) all source- specific import demand prices are obtained from FAOSTAT (2010) database as unit prices; (2) precise shipping cost for USA is 87 US$/MT, obtained from Oosterman (2000); and (3) using Jaffee (2003) as a guide, transportation costs for China and ROW are estimated to be 250 US$/MT each. The sample period for this study is 1995 through 2007. 1.7 Results and Discussion This section shows reduced-form elasticities computed for the entire sample period as well as the three sub-periods. In addition, corresponding economic welfare results are provided. Specifically, Table 1.3 shows results from baseline parameter values together with the three sub-periods in connection with the inception of EU standards harmonization (and tightening). For each period, results attained from incorporating demand interrelationships are juxtaposed against those obtained when peanut is 26 assumed not to be source-differentiated. Finally, Table 1.4 displays approximated exporter and EU consumer welfare changes for the case where substitution effects are ignored in the model. Table 1.3. Price and Quantity Effects of Aflatoxin Regulation in the EU Market, 1995-2007 1995-2007a 1995-1998b 1999-2002c 2003-2007d Variables NSEe SEf NSEe SEf NSEe SEf NSEe SEf PcS* -0.0098 0.0034 -0.0136 -0.0054 -0.0033 0.0101 -0.0109 0.0063 PusS* -0.1409 -0.1382 -0.0834 -0.0738 -0.1381 -0.1701 -0.1887 -0.1759 PrwS* -0.0051 -0.017 -0.0046 -0.0067 -0.0126 -0.0276 -0.0029 -0.0205 PcD* 0.1054 0.113 0.1465 0.151 0.0351 0.043 0.1176 0.1277 PusD* 0.349 0.3503 0.2068 0.213 0.3424 0.3263 0.4671 0.472 PrwD* 0.1995 0.1938 0.1807 0.1796 0.4945 0.4903 0.1124 0.1025 Qc* -0.1837 0.0637 -0.2559 -0.101 -0.0611 0.1904 -0.2042 0.1177 Qus* -0.6518 -0.639 -0.3855 -0.3414 -0.6387 -0.7865 -0.8726 -0.8133 Qrw* -0.0549 -0.1838 -0.0498 -0.0726 -0.136 -0.2983 -0.0309 -0.2209 aBaseline period. bFirst sub-period. cSecond sub-period. dThird sub-period. eResults obtained when substitution effects are ignored (i.e. No Substitution Effects). fResults obtained when substitution effects are included in the analysis (i.e. Substitution Effects considered). From Table 1.3, it is evident that reduced-form elasticities (especially for the model scenario that ignores substitution effects in demand) conform to the expected incidence signs as a result of standards tightening. Thus, in the case where peanuts from different origins are treated as homogeneous, it is observed that regulation tightening causes all export prices and quantities to fall. For example, in the baseline period (i.e. 1995-2007), a 10% increase in the compliance costs associated with aflatoxin regulation tightening causes a 0.098% decrease in China?s export price and a 1.837% drop in its quantity of peanut exported to the EU market. In the aforementioned situation ?? where demand interrelationships are ignored ?? all import demand prices increase. Specifically, import demand price offered to China rises by 1.054% and those of USA and ROW also go up by 3.490% and 1.995%, 27 respectively when standards compliance tax increase by 10%. Comparisons of the magnitude of export price reductions associated with tighter standards indicate that USA faces the most severe price drop while ROW experiences the least impact. It is also important to note that the intensity of the EU aflatoxin policy on import demand prices is greater than that of the corresponding supply prices. In other words, comparing absolute magnitudes of all price effects reveals that demand prices paid by EU consumers are more responsive to standards tightening than corresponding supply prices received by exporters. The preceding observation is in line with the principle of tax incidence given that EU peanut demand elasticity values are consistently less than the export supply elasticity values, in absolute terms (see Table 1.2). Thus, the less elastic side of the peanut trade market in Europe, namely consumers, appears to bear the greater incidence of stricter aflatoxin regulation policies. To highlight short-run effects of the policy, and to further demonstrate the principle that the less elastic side of the market bears the greater incidence, simulations have been provided in Appendix 1D where supply elasticities are set to zero. We observe that in the short run, where supply is perfectly inelastic (and therefore less than demand elasticities in absolute terms), the entire incidence of the policy is borne by exporters in that supply prices are lowered but EU consumer prices remain unaffected. On the other hand, introduction of substitution effects into the model also leads to falling export prices and quantities for USA and ROW, while China enjoys rising export price and quantity. Precisely, a 10 percentage increase in the regulation compliance costs drives USA and ROW export prices down by 1.382% and 0.170%, respectively, whereas that of China moves up by 0.034%. Again, for import demand prices, EU consumers experience increases in peanut prices following aflatoxin regulation tightening. 28 It is worth emphasizing that due to the price-wedge setup, whenever export prices decrease and corresponding demand prices increase following regulation tightening, both sides of the market are deemed to be sharing the economic burden from regulation compliance costs; similar to the distribution of conventional tax incidence. With the foregoing in mind, China rather appears to benefit from standards tightening given that both of its supply and demand prices increase after standards tightening unlike its competitors who bear the effects of compliance tax with EU consumers. China?s apparent gain could be explained by Boonsaeng, Fletcher, and Carpio?s study which found that peanuts from USA and China are highly substitutable and that EU consumers are responsive to price changes. That is, since China?s peanut is consistently cheaper than that of USA (see unit prices in Table1.1), EU consumers are likely to demand more of China?s peanut following increasing regulation. Therefore, the presence of demand interrelationships seems to favor China in the face of higher compliance costs associated with stringent aflatoxin standards. However, USA apparently suffers lesser losses due to substitution effects as opposed to the case where there is no substitution effect, while ROW experiences heavier losses in the presence of demand interrelationships. For deeper insight into how regulation tightening affects prices and quantities in different time periods, attention is extended to sub-period analysis. Particularly, the purpose of conducting the sub-period incidence analyses (see Table 1.3 for results) is to see how changing export requirements in those times (in terms of standards evolution) compare with the economic burden derived for the entire sample period.17 17 The alpha and beta parameters are re-calibrated to reflect sub-period prices (see Appendix for values). However, import demand and export supply elasticities for the entire sample period are maintained for the sub-periods as well (see Appendix 1 for the sub-period export quantity shares which are close to the baseline values). 29 Diop, Beghin and Sewadeh (2004) note that USA and Africa lost market shares in the edible peanut market to Argentina and China over the past two decades (see Appendix 1A). The authors argue that on the part of African exporters, the fall in market share is explained by strict aflatoxin standards, while domestic peanut policies are partly to blame in the case of USA.18 By inspection, results from virtually all three sub-periods exhibit qualitative similarities to the baseline results. In other words, tighter regulations influence on prices and quantities are similar to those of the baseline period discussed earlier. In the scenario where substitution effects are ignored, the results for the sub-periods are close to those of the entire sample period. A closer look reveals that the severity of the regulations effect on China?s export price is most intense in the first sub-period and least in the second sub-period. For the USA, the degree of price lowering intensifies consistently from the first to the last sub-period with the latter effect greater than the baseline. However, in the case where substitution effects are included in the model, the point of departure from the baseline results occurs only in the first sub-period. Specifically, all export prices drop in the first sub-period unlike in the baseline where China enjoys a price increase. Essentially, the baseline results reasonably reflect the effects of regulation tightening on edible peanut prices and quantities in the EU market. Finally, approximated economic welfare changes due to a 10% increase in compliance costs (following regulation tightening) are illustrated in Table 1.4 to underscore the importance of price and quantity effects of the aflatoxin policy. The effects of aflatoxin regulation on all prices and quantities used in the welfare estimation are captured through the reduced-form elasticities shown in Table 1.3. 18For details on aflatoxin standards enforced by the EU, see Otsuki, Wilson and Sewadeh (2001a, 2001b); and Xiong and Beghin (2010). Diop, Beghin and Sewadeh (2004) provide a lengthy discussion on domestic peanut policies for many peanut producing countries including USA. 30 Table 1.4. Welfare Changes (US$) Induced by 10% Regulation Tax Increase in the EU Market, 1995-2007 Exporters Exporter Welfare EU Consumer Welfare China -74850 -3960198 USA -750370 -13000000 ROW -71270 -18000000 Total -896490 -34960198 Note: These welfare values are from the model with no demand interrelationships. Evidently, in the case where demand interrelationships are not accounted for in the model, regulation tightening causes welfare losses to each side of the market ?? both exporters and consumers are adversely impacted. On the side of suppliers, China, USA, and ROW respectively face welfare losses estimated at US$ 74,850; US$ 750,370 and US$ 71,270 while on the demand side EU consumers also lose a total of US$ 34,960,198 in welfare. Therefore, given the price and quantity incidence of aflatoxin regulation tightening, one can accordingly infer that, in general, both sides of the market experience losses in economic welfare regardless of the presence or otherwise of demand interrelationships. 1.8 Summary and Conclusions The main purpose of this chapter was to investigate price and quantity effects triggered by Europe?s stringent aflatoxin regulation policy on peanut imports; in order to shed light on attendant economic welfare impacts. To achieve this goal, the Equilibrium Displacement Modeling technique was employed to evaluate the aflatoxin policy. A major assumption is that edible peanuts from various exporting countries are differentiated according to origin by consumers in the European Union 31 (EU) market. Annual data from the period 1995 through 2007 was obtained from FAOSTAT database in addition to other sources cited in the text. Edible peanut exporters covered in the study are China, USA, and rest of the world (ROW). EU countries form the export market. Overall, it is apparent from baseline results that if peanuts from various countries are assumed to be homogeneous then tighter regulations affect exporters differently as opposed to the case where peanut origins are treated by importers as heterogeneous. That is, in the scenario where substitution effects in the market are ignored, it is clear that tighter regulations depress all export prices and quantities. However, accounting for demand interrelationships reveals that although USA and ROW do experience decreases in export prices and quantities, China actually enjoys rising export price and quantity, following aflatoxin regulation tightening. China?s benefits could be attributed to findings in the literature that edible peanuts from the two leading exporters (i.e. USA and China) are highly substitutable in the EU market. Thus, regulation tightening creates higher compliance cost which is translated into increased demand prices; causing importers to substitute away from USA and ROW toward China. Given evidence that peanuts in the EU market are source- differentiated, the latter result is revealing. Contrary to popular belief that strict aflatoxin regulation hurts all exporters in terms of lost revenues, it has been shown that, in fact, some exporters (such as China) do benefit. Interestingly, USA which is a rich exporter suffers losses together with ROW (which is predominantly composed of developing country exporters) when standards are raised. Comparisons of the magnitude of export price reductions due to the aflatoxin policy indicate that USA faces the most severe price lowering effect, while ROW experiences the least impact. 32 However, USA is apparently harmed less due to substitution effects in demand as opposed to the case where there is no substitution effect, whereas ROW incurs greater losses in the presence of demand interrelationships compared to the other model scenario. It is worth stressing that most of the ROW countries are only fringe suppliers of edible peanut to the EU market unlike major exporters, namely China and USA (see Appendix 1A). Hence, the apparent welfare loss to ROW may not be substantial when disaggregated to the country level. In general, regulation tightening for peanut aflatoxin (which is modeled in this study as import tax) depresses export prices and raises demand prices in the EU market. Also, results confirm the tax incidence principle that the less elastic side of a market (i.e. the demand side of the peanut market) bears the greater incidence of interventions. Thus, given that absolute values of peanut demand elasticities are less than supply elasticities in the EU market, consumers consequently experience greater price and quantity effects from standards tightening than exporters. Hence, EU consumers ultimately pay most of the costs from the aflatoxin policy. Furthermore, regarding the economic welfare implications of strict standards, price and quantity effects evaluated in theoretically-consistent welfare formulas highlight findings that all sides of the peanut trade suffer economic losses. Therefore, based on the findings of this chapter, strict aflatoxin standards imposed on peanut trade hurts each side of the international market since some exporters lose revenue, whereas consumers in importing countries face higher retail prices. As presented in this chapter, the greater share of the economic burden owing to aflatoxin standards tightening is borne by EU consumers. Exporters? economic impacts are modest compared to EU consumers who bear the major costs of the aflatoxin intervention. 33 Hence, the market can be interpreted as fair and efficient; since EU consumers who are the intended beneficiaries of the strict aflatoxin standards also pay the greater share of the compliance cost. Findings from this chapter underscore the need for closer collaboration among trading countries, both exporters and importers, with a collective goal of effectively controlling the aflatoxin contamination problem; such partnerships would be helpful to all parties involved in cross-border trade. The interaction may include the transfer of technical know-how, assistance with requisite resources, and standards harmonization. Moreover, in order to minimize the attendant economic losses to either side of the market, policy makers would have to enforce realistic aflatoxin standards scientifically proven to be safe to consumers. Thus, the negative economic welfare implications resulting from strict aflatoxin interventions provide guidance to policy makers in rich importing countries to implement standards that do not harm trade partners and consumers. 34 Chapter 2: Economic Analysis of Aflatoxin Interventions in Developing Countries: The Peanut Sector in Ghana 2.1 Background and Problem Statement Peanut (Arachis hypogaea), also known as groundnut, is an important food crop produced in many Sub-Saharan African countries. Due to heavy domestic consumption, proportions traded internationally are often low (Diop, Beghin and Sewadeh, 2004). Examples of food products derived from peanut include butter, confectionaries, oil, and cake. Promising markets exist for peanut particularly in the snack food industries in North America and Europe, and in popular Asian cuisines (ARD, 2008). Peanut is a key source of protein in Ghana. The crop is dominantly grown in the northern regions of the country (Atuahene-Amankwa, Hossain, and Assibi, 1990). Figure 2.1 displays peanut production and demand in Ghana from 1995 through 2008. Domestic consumption of the crop is high and nearly matches total production. The chart generally reveals an increasing trend in supply and local consumption, in spite of fluctuations over the period. Cross-border trade in peanut (including peanut oil) is marginal and fairly stable over the period in question. Similarly, Table 2.1 reveals that Ghana is largely a net peanut exporter although quantity traded abroad is only a minute fraction relative to that of the domestic market. 35 Figure 2.1. Peanut Production and Distribution in Ghana from 1995-2008 Source: Computed from FAO Statistics (2011) Table 2.1 Peanut Production Quantities in Ghana; 1995-2008 Year Production Export Import 1995 168201 1 1 1996 161631 201 1 1997 153649 47 1 1998 212491 7 12 1999 193001 44 1 2000 209001 1199 279 2001 258001 178 2 2002 520001 1104 1 2003 439001 11001 38 2004 389650 14584 505 2005 420001 6462 1 2006 520001 3319 1 2007 301771 1324 1 2008 470101 648 96 Note: Quantities are in Metric Tonnes. Source: FAO Statistics (2011) -100000 0 100000 200000 300000 400000 500000 600000 1994 1996 1998 2000 2002 2004 2006 2008 2010 Me tri c T on ne s Year Production Export Import 36 As observed in most basic food staples in the developing world, peanut is susceptible to mycotoxin contamination; especially aflatoxins (Jolly et al., 2006).19 Environmental conditions in tropical and sub-tropical regions of the world ?? high temperature and humidity, insect infestation, improper hygiene, among others ?? are known to be conducive to the growth of mycotoxin-producing fungi (Dohlman, 2003). Hence, mycotoxins are more likely to present challenges to public health and economic welfare of populations living in warm countries. Extensive research show strong association between aflatoxins exposure and a host of negative health outcomes (Wang et al., 2001; Turner et al., 2003; Williams et al., 2004; Lewis et al., 2005; Wu, 2006; Liu and Wu, 2010; Wu and Khlangwiset, 2010). Although aflatoxins and other mycotoxins impact people across the world, the problem deserves urgent attention in developing countries, particularly for the following reasons: (1) Food items that serve as basic staples to consumers are prominent substrates for toxin- producing fungi (Wang et al., 2001; Shephard, 2003); (2) Low-income conditions generally compel individuals to consume contaminated food due to availability of limited resources (Jolly et al., 2006); (3) Given that the mycotoxin problem is pronounced among low-income populations, and since greater percentage of the world?s population reside in developing countries (Todaro and Smith, 2012), one would expect that most of the people exposed to high levels of mycotoxins live in those regions; and (4) Incidence and prevalence of Hepatitis B is disproportionately high in low-income countries (WHO, 2008), and this has been shown to be strongly 19 Mycotoxins are composed of chemical substances produced by fungi which contaminate crops during production and after harvest. Aflatoxins of concern are designated B1, B2, G1 and G2 (Park et al., 2002). 37 associated with high burden of diseases such as liver cancers (Montesano, Hainaut, and Wild, 1997; Wild and Hall, 1999; and Dash et al., 2007). To protect public health, regulatory bodies respond to the mycotoxin problem through the introduction and enforcement of residue regulation policies. Although international food standards exist, the World Trade Organization also allows countries to enforce own standards when necessary.20 For example, the international aflatoxin standard set by Codex is 15 ppb, whereas the European Union (EU) enforces 4ppb, and the United States requires 20 ppb (Otsuki, Wilson and Sewadeh, 2001a, 2001b).21 The presence of aflatoxin regulations in developed countries such as Europe and the United States is a manifestation of heightened interest in food safety. Ultimately, the goal is to establish uniform mycotoxin standards worldwide (Council for Agricultural Science and Technology, 2003). Therefore, with increasing globalization, it is inevitable that developing countries will pursue regulation policies in harmony with rich countries ?? to safeguard public health and promote trade locally and abroad. Against this backdrop, this chapter isolates Ghana (because of its high peanut consumption) to study possible economic impact of the aflatoxin contamination problem on its populace. According to FAO (2004), Ghana has no comprehensive national mycotoxin standards that cover peanut aflatoxin in detail. However, Ghana is building capacity on Sanitary and Phytosanitary Standards (SPS) with support from the Danish International Development Agency; as part of a Trade Sector Support 20 Each nation is permitted to set its own level of maximum contamination according to the ?Precautionary Principle?. This principle is essentially the World Trade Organization?s recognition of a country?s right to issue standards to protect its people: 1) whenever it deems necessary and 2) with no obligation to show any scientific proof of the potential threat (Yue, Beghin and Jensen, 2006). 21 Codex refers to the joint FAO/WHO Codex Alimentarius Commission responsible for setting food standards. For details, see Otsuki, Wilson and Sewadeh, 2001a, 2001b. Also, ppb means ?parts per billion?. 38 Program (TSSP). Under the TSSP, there are a number of SPS projects with one specifically geared toward enforcement in domestic markets and eventual harmonization with West African countries (Ministry of Food and Agriculture, 2008). As Ghana prepares to enforce mycotoxin standards, it is important to assess possible policy implications given that food standards introduce additional costs into the supply chain. The majority of studies on food standards focus entirely on trade volume effects (Otsuki, Wilson and Sewadeh, 2001a, 2001b; Yue, Beghin and Jensen, 2006; Nogueira et al., 2008; Nguyen and Wilson, 2009), much to the neglect of price effects on market participants. Moreover, benefits of aflatoxin standards to populations in developing countries ?? due to the assurance of safer food ?? are mostly ignored. Thus, to a large extent, the literature on regulations is devoted to effects on exporters trading with rich countries that impose strict standards. The literature?s focus on trade flows has, therefore, led to limited attention on how economic welfare of domestic producers and consumers in developing countries is affected by the mycotoxin problem. Consequently, the impacts of aflatoxin regulations on food prices are largely unknown although prices are crucial to understanding the economic implications of policy interventions, as far as market participants within a country are concerned. Furthermore, to the best of my knowledge, the potential benefits associated with consumption of peanut with reduced contamination (i.e. quality peanut) have not been incorporated into any evaluation of the economic impacts driven by aflatoxin policy interventions. 39 2.2 Objectives The purpose of this chapter is to determine the economic impacts of aflatoxin regulation on peanut market participants in Ghana. Also, I explore how trade status and the demand for quality peanut affect distribution of economic gains/losses among domestic market participants. Thus, the primary goal is to gain insights into the incidence of economic burden or benefits from the aflatoxin policy intervention, whether the peanut sector is closed or open to cross-border trade. 2.3 Theoretical and Conceptual Framework The theoretical foundation of this study hinges on the standard theories of tax incidence and economic equilibrium introduced in Chapter One. Similarly, this chapter applies the tariff equivalent method to quantifying SPS compliance costs using a price-wedge approach (Calvin and Krissoff, 1998; and Yue, Beghin and Jensen, 2006). In Ghana, the contribution of the peanut sector to the national economy is not substantial. Hence, economic incidence of the aflatoxin policy is analyzed in a partial equilibrium setting as opposed to a general equilibrium framework (Kotlikoff and Summers, 1987). To characterize the model, the following simplifying assumptions are made about the peanut market in Ghana: (1) The market is perfectly competitive; (2) Complete market clearing occurs in equilibrium; (3) There are parallel shifts in demand and supply curves in response to shocks on equilibrium caused by exogenous factors such as the aflatoxin-compliance tax. In the literature, economic incidence analysts including Alston, Norton and Pardey (1995); Wohlgenant (1999); and Sun and Kinnucan (2001) have imposed similar assumptions. 40 Another key assumption in this study is that regulation tightening leads to increases in compliance costs along the peanut supply chain (Maskus and Wilson, 2001; Otsuki, Wilson and Sewadeh, 2001a). Mycotoxins contamination largely occur through post-harvest practices such as mode of transportation; storage type and duration; handling methods during processing and marketing; among others (Dohlman, 2003; Amoako-Attah et al., 2007; N?dede et al., 2012). In fact, studies conducted in Ghana (Amoako-Attah et al., 2007) and Benin (N?dede et al., 2012) indicate that effective post-harvest handling measures, such as proper drying and sorting, do attract additional peanut supply costs. As a result, regulation compliance cost is treated in the analysis as tax (i.e. shock variable) imposed on the production side that shifts the peanut supply curve inwards; following the exogenous policy intervention. Hence, the economic incidence of aflatoxin compliance costs is assumed to be similar to standard tax incidence. Compliance costs, in connection with food standards, refer to all expenses incurred by the private and public sectors within a particular supply chain.22 Thus, cost outlays and losses borne by peanut suppliers ?? due to aflatoxin standards ?? are considered as compliance costs. It is also important to stress that stricter standards will necessitate higher expenditure from food suppliers in order to comply with such regulations. Consequently, compliance costs for EU standards would be higher than those associated with the United States and Codex?s standards. Notable elements of compliance costs for peanut aflatoxin regulations involve expenses on sorting; drying; proper storage and materials; and good hygiene practices. Moreover, peanut supplies that fail to meet standards for human food are usually relegated to financially-unattractive outlets such as animal feed, biofuels, or completely discarded leading to revenue losses. 22 See Shafaeddin (2007) for more on SPS compliance costs. Also, to understand the broad nature of mycotoxin control strategies, see Dohlman (2003) and Codex Alimentarius Commission (2003). 41 For example, Jolly et al. (2010) show that the high aflatoxin contamination found in peanuts in Ghana means that suppliers would have to discard over 50% per standards from EU, World Health Organization, and the United States; with EU regulations causing the greatest losses. In short, this chapter assumes that if a country moves away from a no-standards policy regime and adopts any of the three standards mentioned, then market participants would face compliance costs/tax. Similarly, if a country switches away from lenient standards to more stringent ones, the market participants in question, again, deal with higher aflatoxin compliance costs. Furthermore, it is assumed that all peanut suppliers are legally obliged to conform to regulations enforced by the Ghana Standards Authority. Therefore, transactions between sellers and buyers would occur only after inspection and approval from the national regulatory body. This working assumption circumvents possible transactions that may occur in the informal (or unregulated) markets thereby simplifying the analyses. 2.4 Method and Models The distributional impact of aflatoxin regulation is operationalized in an Equilibrium Displacement Model (EDM) framework. Thus, this chapter determines price and quantity effects of Ghana?s domestic aflatoxin-compliance tax using EDM method, where policy evaluation is conducted within a system of supply and demand (see Piggot, 1992; Davis and Espinoza, 1998; Metcalfe, 2002; Kinnucan and Myrland, 2005). Structural demand and supply elasticity estimates are obtained from the literature and used in the model to generate reduced-form price and quantity impacts, following marginal increases in aflatoxin compliance costs. 42 The results demonstrate how peanut producer and consumer prices are impacted by a rise in compliance costs associated with aflatoxin regulation in Ghana. Price and quantity effects are subsequently translated into approximate welfare losses or gains. In what follows, the peanut sector in Ghana is introduced in three nested models conditional on trade status and/or consumer preference for quality. Thus, economic implications of the aflatoxin policy are evaluated such that the distribution of losses or gains is determined for a closed sector versus the case where the peanut industry is open to cross-border trade (i.e. market liberalization). 2.4.1 Model One: Autarky in the Peanut Market This model assumes that Ghana is self-sufficient in the supply of peanut. Table 2.1 (and Figure 2.1) shows that domestic production and consumption are nearly identical since exports form a small fraction of supply. The apparent self-sufficiency in the sector provides the basis for analyzing a closed economy. Initial equilibrium in the autarkic sector is represented by the following structural model: g4666uni0031g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3005 g3404g1843g3005g4666g1842g3005uni0009g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1856g1857g1865g1853g1866g1856g4669 g4666uni0032g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3020 g3404g1843g3020g4666g1842g3020uni0009g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1871g1873g1868g1868g1864g1877g4669 g4666uni0033g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3005 g3404g1842g3020 g3397g1846g3397g1844uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1868g1870g1861g1855g1857uni0009g1864g1861g1866g1863g1853g1859g1857g4669 g4666uni0034g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3020 g3404g1843g3005 g3404g1843uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1865g1853g1870g1863g1857g1872uni0009g1855g1864g1857g1853g1870g1861g1866g1859g4669 where QD denotes quantity of peanut demanded, QS represents quantity of peanut supplied, PD is the consumer price of peanut in the Ghanaian market, PS is the producer price received by peanut suppliers, T is the per-unit transaction costs incurred as peanuts are moved from producers to final consumers excluding standards compliance costs, and R is the per-unit compliance costs or ?tax? that captures aflatoxin regulation tightening. 43 The subscripts D and S denote demand and supply, respectively. Detail information on the variables is provided in Table 2.2. Endogenous variables in the model are QD, QS, PD, and PS, whereas T and R are exogenous. Regulation compliance tax is the exogenous variable of interest although there are several others that have been suppressed for simplicity. Equation (1) is the demand equation. The equation states that the quantity of edible peanut demanded in Ghana is determined by the retail price. Furthermore, the quantity of peanut produced is assumed to be influenced by the supply price. Equation (2) represents the supply equation. Producer price excludes costs required to transfer peanut from the farm to the consumer (including cost of complying with the standards). The price-wedge equation, i.e. (3), accounts for the relationship between supply and demand prices in the Ghanaian market. The price differential is attributed to market transaction costs, including costs associated with aflatoxin minimization practices such as proper drying.23 Equation (3) provides the important link to modeling the regulation as a tax and ultimately distinguishing consumer price effects from those of producers. Finally, a market clearing condition is imposed in equation (4) to ensure that the system is closed. Stated differently, in equilibrium, quantity demanded is exactly identical to quantity supplied in the market. 23 Henceforth, other transaction costs are suppressed since the primary focus is on compliance costs induced by regulation tightening. The basic price equation is g1842g3005 g3404g1842g3020 g3397g1846g3397g1844 and after suppressing transaction costs, the price equation is written in percentage changes as in equation (3?). 44 Table 2.2. Average Peanut Market Information in Ghana, 1995 to 2008 Variable Definition Value QD Quantity demanded domestically in Ghana (MTa) 312497.10 QX Quantity exported abroad (MT) 2864.64 QS Total quantity produced in Ghana (MT) 315463.40 PD Price paid by consumers in Ghana (US$/MT) 796.00 PS Price received by peanut producers in Ghana (US$/MT) 610.87 T Transaction costs (US$/MT) 186.00 R Aflatoxin regulation compliance costs (US$/MT) 50.22 Source: Computed from FAO Statistics (2011). Note: aMT denotes metric tonnes. Next, the structural model is expressed in percentage changes (or displaced form) as shown below: g4666uni0031g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3005uni2217 g3404uni0009uni0009g2015g3005g1842g3005uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2015g3005 g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1856g1857g1865g1853g1866g1856g4669 g4666uni0032g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3020uni2217 g3404uni0009g2013g3020g1842g3020uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2013g3020 g3410uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1871g1873g1868g1868g1864g1877g4669 g4666uni0033g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3005uni2217 g3404uni0009g2009g1842g3020uni2217 g3397g2010g1844uni2217uni0009uni0009g1875g1860g1857g1870g1857uni0009g2009 g3404 g1842g3020g1842 g3005 g3408uni0030uni0009g1853g1866g1856uni0009g2010 g3404 g1844g1842 g3005 g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1868g1870g1861g1855g1857uni0009g1864g1861g1866g1863g1853g1859g1857g4669 g4666uni0034g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3020uni2217 g3404uni0009uni0009g1843g3005uni2217 g3404uni0009g1843uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1865g1853g1870g1863g1857g1872uni0009g1855g1864g1857g1853g1870g1861g1866g1859g4669 Table 2.3 provides information on parameters in the displaced model. Asterisks attached to the variables denote percentage changes. For example, g1842uni2217uni0009g3404 g3031g3017g3017 . The displaced model consists of four endogenous variables, namely uni0009g1843g3020uni2217uni0009uni002C uni0009g1843g3005uni2217uni0009uni002Cg1842g3020uni2217uni0009g1853g1866g1856uni0009g1842g3005uni2217 , in four equations. The only exogenous variable in the model is g1844uni2217 given that all other exogenous variables are suppressed. Since the current study models regulation tax as a supply shifter, we solve for g1842g3020uni2217 in g4666uni0033g4593g4667 and substitute the result into uni0009g4666uni0032g4593g4667 to obtain the tax-burdened supply equation. Precisely, uni0009g1843g3020uni2217 is rewritten as a function of uni0009g1842g3005uni2217 and g1844uni2217 as illustrated below: g4666uni0035g4667uni0009uni0009uni0009uni0009g1842g3020uni2217 g3404uni0009uni0009g1842g3005 uni2217 g2009 g3398 g2010 g2009g1844 uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 45 Therefore, plugging g4666uni0035g4667 into g4666uni0032g4593g4667 yields the following supply equation; g4666uni0036g4667uni0009uni0009uni0009uni0009g1843g3020uni2217 g3404uni0009g2013g3020g2009 uni0009g1842g3005uni2217 g3398g2013g3020g2010g2009 g1844uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1872g1853g1876g3398g1854g1873g1870g1856g1857g1866g1857g1856uni0009g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1871g1873g1868g1868g1864g1877g4669 Table 2.3. Parameter Values Parameter Definition Value g2751D Own-price elasticity of domestic demand -0.2000 g2751X Own-price elasticity of export demand -1.9000 g2239S Own-price elasticity of domestic supply 0.3500 g2239X Own-price elasticity of export supply 25.8740 g2009 Price transmission elasticity 0.7663 g2746 Aflatoxin regulation compliance tax rate 0.0631 KD Share of domestic production consumed locally 0.9900 KX Share of domestic production exported 0.0100 Notes: See the data section below for sources and computation of entries in the table. Applying simple algebra, the endogenous variables in the displaced model are simultaneously solved for as reduced-form elasticities (expressed as functions of structural elasticities in the model). Matrix algebra and spreadsheets are typically employed in sophisticated models with a system of equations (for details see Kinnucan and Myrland, 2002). The simple models in this study provide the opportunity to manually solve for the reduced-form elasticities and illustrate how the model works through comparative statics. Figure 2.2 shows the distribution of economic burden in autarky following enforcement of regulation policy in Ghana. 46 Figure 2.2. Incidence of Aflatoxin Tax on the Peanut Sector in Ghana 2.4.1.1 Comparative Statics and Computation of Reduced-Form Elasticities This section provides analytical solutions for the reduced-form elasticities. In addition, the comparative statics demonstrate how the model works in deriving economic incidence relationships, as shown graphically in Figure 2.2 above. We proceed by substituting g4666uni0036g4667 and g4666uni0031g4593g4667 into g4666uni0034g4593g4667 and subsequently solving for consumer price effect as follows: g4666uni0037g4667uni0009uni0009uni0009uni0009uni0009g1842g3005 uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010 g2013g3020 g3398g2009g2015g3005uni0009g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 At this point, g4666uni0037g4667 is in reduced-form hence plugging it into any of the remaining price and quantity equations in the displaced model yields the other relevant reduced-form elasticities. We, therefore, derive the reduced-form elasticity for producer price by substituting g4666uni0037g4667 into g4666uni0035g4667 which gives the following; P Q P0 Q0 D S0 S1 Q1 PD PS R e0 e1a b 47 g4666uni0038g4667uni0009uni0009uni0009uni0009g1842g3020 uni2217 g1844uni2217 g3404uni0009 g2010g2015g3005 g2013g3020 g3398g2009g2015g3005uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Finally, quantity effects are obtained by putting g4666uni0037g4667 into g4666uni0031g4593g4667 for demand elasticity, and similarly, g4666uni0038g4667 into g4666uni0032g4593g4667 for supply elasticity: g4666uni0039g4667uni0009uni0009uni0009uni0009uni0009g1843g3005 uni2217 g1844uni2217 g3404uni0009 g1843g3020uni2217 g1844uni2217 g3404uni0009 uni0009g1843uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010g2015g3005 g2013g3020 g3398g2009g2015g3005 g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 From the comparative statics results and the graphical analysis provided in Figure 2.2, the following hypotheses are derived about the economic incidence of the aflatoxin compliance tax: (a) consumer prices will rise, from P0 to PD; (b) supply prices will drop, from P0 to PS; (c) quantity of peanuts traded in Ghana will decrease, from Q0 to Q1; (d) the effect of regulation tax, R, is shared between peanut producers and consumers in regions ?b? and ?a?, respectively; and (e) the impact of the policy crucially depends on the relative magnitudes of elasticities. To emphasize the importance of the latter statement, we assume that the supply of peanut is perfectly inelastic in the short run (i.e.uni0009g2013g3020 g3404uni0030). The short-run assumption reveals that producers alone bear all the consequences of compliance tax increases, while the incidence on consumers is nil (see equations g4666uni0037g4667 and g4666uni0038g4667 when g2013g3020 g3404uni0030). Thus, the less elastic side of the peanut market in Ghana ?? suppliers or consumers ?? will experience the greater impact from the aflatoxin policy intervention. 2.4.2 Model Two: Small Open Economy; Exporter with Supply Shift In model Two, the autarky assumption imposed earlier in Model One (that Ghana is self-sufficient and has a closed peanut sector) is relaxed. Evidently, one can argue that Ghana?s peanut sector is not isolated from the rest of the world (see Table 2.1). 48 In other words, peanuts are imported in times of deficit and exports occur in the presence of surplus. Accordingly, we model Ghana as a small open economy, and a net exporter of peanut to understand the incidence of regulations under this scenario. Notice that Ghana is a net exporter of peanut since production slightly outweighs domestic consumption, and exports consistently exceed imports (see Figure 2.1 and Table 2.1). In addition, no single African country is known to be a major player in edible peanut trade. In fact, African countries are fringe suppliers of edible peanut in international trade relative to dominant suppliers, namely China and the United States (Diop, Beghin and Sewadeh, 2004; Boonsaeng, Fletcher and Carpio, 2008). These reasons reinforce the assertion that Ghana is a small player in the international peanut market. One major assumption in this model is that Ghanaian peanut exporters face a perfectly elastic export demand abroad (see Figure 2.3 below for a pictorial illustration of standards effects in this open-market case). Also, this model assumes that the aflatoxin regulation policy, which introduces compliance costs (or tax), is a supply shifter affecting only peanut supply costs but not consumer preference for quality. Initial equilibrium in the extended model is as follows: g4666uni0031uni0030g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3005 g3404g1843g3005g4666g1842g3005uni0009g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1856g1857g1865g1853g1866g1856g4669 g4666uni0031uni0031g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3025 g3404g1843g3025g4666g1842g3025uni0009g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1857g1876g1868g1867g1870g1872uni0009g1856g1857g1865g1853g1866g1856g4669 g4666uni0031uni0032g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3020 g3404g1843g3020g4666g1842g3020uni0009g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1871g1873g1868g1868g1864g1877g4669 g4666uni0031uni0033g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3005 g3404g1842g3020 g3397g1846g3397g1844uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1868g1870g1861g1855g1857uni0009g1864g1861g1866g1863g1853g1859g1857g4669 g4666uni0031uni0034g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3005 g3404g1842g3025uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1864g1853g1875uni0009g1867g1858uni0009g1867g1866g1857uni0009g1868g1870g1861g1855g1857g4669 g4666uni0031uni0035g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3020 g3404g1843g3005 g3397g1843g3025uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1865g1853g1870g1863g1857g1872uni0009g1855g1864g1857g1853g1870g1861g1866g1859g4669 49 where QX denotes quantity of peanut exported to consumers abroad, PX is the consumer price of peanut in the international market, and the remaining variables are defined in Model One. The subscript X represents export. Additional endogenous variables (relative to Model One) are QX and PX ; while the exogenous variables remain as before. Equation (11) is the export demand equation where the quantity of peanuts purchased by consumers abroad is influenced by the international price. Also, Equation (14) imposes the Law of one Price (LOP). This implies that domestic and export consumer prices are identical. Again, Equation (15) is the market clearing condition; total peanut from suppliers in Ghana (producers) equals total demand from domestic consumers as well as demand from buyers abroad. The remaining equations are the same as in Model One. Next, Model Two is rewritten in displaced form as follows: uni0009g4666uni0031uni0030g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3005uni2217 g3404uni0009uni0009g2015g3005g1842g3005uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2015g3005 g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1856g1857g1865g1853g1866g1856g4669 uni0009g4666uni0031uni0031g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3025uni2217 g3404uni0009uni0009g2015g3025g1842g3025uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2015g3025 g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1857g1876g1868g1867g1870g1872uni0009g1856g1857g1865g1853g1866g1856g4669 uni0009g4666uni0031uni0032g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3020uni2217 g3404uni0009g2013g3020g1842g3020uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2013g3020 g3410uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1871g1873g1868g1868g1864g1877g4669 g4666uni0031uni0033g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3005uni2217 g3404uni0009g2009g1842g3020uni2217 g3397g2010g1844uni2217uni0009uni0009g1875g1860g1857g1870g1857uni0009g2009 g3408uni0030uni0009g1853g1866g1856uni0009g2010 g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1868g1870g1861g1855g1857uni0009g1864g1861g1866g1863g1853g1859g1857g4669 g4666uni0031uni0034g4593g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3005uni2217 g3404uni0009g1842g3025uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1864g1853g1875uni0009g1867g1858uni0009g1867g1866g1857uni0009g1868g1870g1861g1855g1857g4669 g4666uni0031uni0035g4593g4667uni0009uni0009uni0009g1843g3020uni2217 g3404g1837g3005g1843g3005uni2217 g3397uni0009g1837g3025g1843g3025uni2217 uni2236uni0009uni0009uni0009g1837g3005 g3404g1843g3005g1843 g3020 g3408uni0030uni0009uni003Buni0009g1837g3025 g3404g1843g3025g1843 g3020 g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1865g1853g1870g1863g1857g1872uni0009g1855g1864g1857g1853g1870g1861g1866g1859g4669 where g1837g3005 and g1837g3025 are parameters defined in Table 2.3. In an open economy, equilibrium in trade is required to complete the model. This is achieved as follows: First, we derive the implied export supply equation by dropping g4666uni0031uni0031g4593g4667 so that g1842g3025 is temporarily treated as exogenous (see Muth, 1965). 50 Next, we solve the model simultaneously using the remaining equations to derive the export supply, g1843g3025uni2217. Specifically, g4666uni0031uni0030g4593g4667 and g4666uni0031uni0032g4593g4667 are substituted into g4666uni0031uni0035g4593g4667 and, thereafter, information in g4666uni0031uni0033g4593g4667 and g4666uni0031uni0034g4593g4667 are applied yielding the following supply relation: g4666uni0031uni0036g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3025uni2217 g3404uni0009uni0009g2013g3025g1842g3025uni2217 g3398uni0009g2013g3020g2010g2009g1837 g3025 g1844uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g2013g3025 is the export supply elasticity expressed as follows; g4666uni0031uni0037g4667uni0009uni0009uni0009uni0009uni0009uni0009g2013g3025uni0009uni0009g3404uni0009uni0009g3084g3268g2879g3080g3012g3253g3086g3253g3080g3012 g3273 uni0009g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Next, equilibrium in the peanut export market is imposed by equating g4666uni0031uni0036g4667 and g4666uni0031uni0031g4593g4667. Subsequently, reduced-form elasticities for the two consumer prices are obtained as follows: g4666uni0031uni0038g4667uni0009uni0009uni0009uni0009uni0009uni0009g1842g3025 uni2217 g1844uni2217 g3404 uni0009g1842g3005uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667uni0009g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Alternatively, we can use information in g4666uni0031uni0037g4667 to rewrite g4666uni0031uni0038g4667 as shown below: g4666uni0031uni0038uni2032g4667uni0009uni0009uni0009uni0009uni0009uni0009g1842g3025 uni2217 g1844uni2217 g3404 uni0009g1842g3005uni2217 g1844uni2217 g3404 g2013g3020g2010 g2013g3020 g3398g2015uni0009g3408uni0030uni0009uni0009g1875g1860g1857g1870g1857uni0009g2015 g3404uni0009g2009g4666g1837g3005g2015g3005 g3397g1837g3025g2015g3025g4667g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 and noting that g2015 is the overall demand elasticity faced by peanut producers in Ghana. Following the approach in Model One, the remaining price and quantity effects are derived as shown below: g4666uni0031uni0039g4667uni0009uni0009uni0009uni0009g1842g3020 uni2217 g1844uni2217 g3404uni0009 g2010 g2009uni0009g3428 g2015 g2013g3020 g3398g2015g3432uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0032uni0030g4667uni0009uni0009uni0009uni0009g1843g3005 uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010g2015g3005 g2013g3020 g3398g2015uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0032uni0031g4667uni0009uni0009uni0009uni0009g1843g3025 uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010g2015g3025 g2013g3020 g3398g2015uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0032uni0032g4667uni0009uni0009uni0009uni0009g1843g3020 uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010 g2009 uni0009g3428 g2015 g2013g3020 g3398g2015g3432uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 51 The comparative statics results derived here are consistent with those in Model One in that consumer prices increase following increases in compliance cost, while supply prices drop. Given that Ghana is a small peanut exporter, the above policy incidence relationships (i.e. equations (18) through (22)) respectively reduce to the following:24 uni0009g3017g3273uni2217 g3019uni2217 g3404 uni0009g3017g3253uni2217 g3019uni2217 g3404uni0030; g3017g3268uni2217 g3019uni2217 g3404 g3081 g2879g3080 g3407uni0030; uni0009g3018g3253uni2217 g3019uni2217 g3404uni0030 ; uni0009g3018g3273uni2217 g3019uni2217 g3404uni0030; and g3018g3268uni2217 g3019uni2217 g3404uni0009 g3084g3268g3081 g2879g3080 g3407uni0030. In addition, Figure 2.3 visually illustrates the economic incidence of the aflatoxin policy with Ghana as a small peanut exporter facing a perfectly elastic demand in the world market. Note that the first panel in Figure 2.3 represents the domestic peanut market while the second panel depicts the export market. Also, ?ES? denotes Export Supply (i.e. Excess Supply) and ?ED? means Export Demand (or Excess Demand). 24 Using L'H?pital's Rule, the numerator and denominator of equations (18) through (22) are separately differentiated with respect to g2015 since a perfectly elastic export demand means g2015g3025 g3404g3398uni221E which also implies that g2015 g3404g3398uni221E. 52 Figure 2.3. Incidence of Peanut Aflatoxin Tax on Ghana?s Domestic and Export Markets The incidence analyses ?? provided analytically above and graphically in Figure 2.3 ?? indicate that producers would experience the full economic burden of the aflatoxin regulation with no impact at all on consumers. Thus, increased compliance costs from the policy will lead to a decrease in producers? profits since peanut supply prices would be depressed in order to accommodate the additional costs. 2.4.2.1 Measurement of Economic Welfare From the export market in Figure 2.3 above, we observe that suppliers? pre-tax economic welfare is approximated by the area of triangle PXDe0a, whereas the post-tax welfare is identical to the area of triangle PXSfa. Since export demand is perfectly elastic, peanut suppliers bear the full burden of the tax which translates into a loss in P Q D S0 S1 PD PS QX PX ED ES0 QX0 PXD QS0QD ES1 QX1QS1 R e0 PXS e1 a b Export MarketDomestic Market f 53 economic surplus approximated by the difference between the areas of triangles PXDe0a and PXSfa. This reduction in producer welfare is clearly seen in Figure 2.3, where the economic surplus before the tax is greater than after the intervention. The economic welfare changes for both sides of the market are computed by adapting formulas in Sun and Kinnucan (2001). Modeling the aflatoxin compliance tax as a supply shifter, the formulas for producer and consumer welfare changes are as follows; g4666uni0032uni0033g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1986g1829g1845g3404g3398g1842g3005g1843g3005g1842g3005uni2217uni0009g4666uni0031g3397uni0030uni002Euni0035g1843g3005uni2217uni0009g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0032uni0034g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1986g1842g1845g3404g4666g1842g3020uni2217 g3398g1848g3020uni0009g4667g1842g3020g1843g3020g4666uni0031g3397uni0030uni002Euni0035g1843g3020uni2217uni0009g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g1986g1842g1845 is change in producer surplus (welfare); g1986g1829g1845 represents change in consumer surplus; g1848g3020 is the vertical shift in the peanut supply curve as a result of regulation compliance costs (see Appendix 2 for the derivation of g1848g3020) ; and g4666uni0032uni0035g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1848g3020 g3404g2010g1844uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 2.4.3 Model Three: Small Open Economy; Exporter with Supply and Demand Shifts The present model is a less restrictive form of Model Two in that it incorporates an upward demand shift to account for potential improvements in peanut quality, as a result of the regulation enforcement. Therefore, Model Three extends the preceding model by determining the distributional impact of the aflatoxin policy when supply and demand curves shift simultaneously. The assumed shifts in supply and demand are supported by Otsuki, Wilson and Sewadeh (2001a, p. 272) who note that ?on the demand side, tighter standards imply higher product quality, thereby increasing demand. [Whereas] on the supply side, tighter standards work as a barrier to trade, as 54 they tend to lead to ? the imposition of higher compliance costs?. To characterize this extension, quality assurance from the regulation is assumed to vertically shift demand upwards in the direction of the price axis (for a discussion of vertical shift parameters, see Muth, 1965; Kinnucan, Xiao, and Yu, 2000). Thus, following Muth (1965), vertical/proportionate shift parameters associated with increase in demand for aflatoxin-free peanut are included in the displaced form of Model Two. For instance, the inverse domestic demand shown below in equation (26) is derived from the domestic demand in equation (10?); with a vertical shift parameter appropriately taken into account: g4666uni0032uni0036g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3005uni2217 g3404uni0009uni0009uni0009 uni0031g2015 g3005 g1843g3005uni2217 g3397g2012uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2015g3005 g3407uni0030uni0009g1853g1866g1856uni0009g2012 g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 and ? is the vertical (proportionate) shift in the domestic demand curve induced by quality assurance from the aflatoxin standard, holding g1843g3005 constant at its initial equilibrium level. The sign of ? is assumed to be positive, reflecting the upward (or increased) shift in demand for quality peanut, and the resulting increase in consumer price. For example, if ? = 0.10 then the regulation is interpreted as shifting the demand curve in the price direction by 10% due to the rise in consumer demand for quality peanut. Thus, this vertical demand shift is expected to cause increases in retail price in the process. Solving equation (26) for g1843g3005uni2217 yields the following ordinary demand curve: g4666uni0032uni0037g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3005uni2217 g3404uni0009uni0009g2015g3005g4666g1842g3005uni2217 g3398g2012g4667uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2015g3005 g3407uni0030uni0009g1853g1866g1856uni0009g2012 g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Subsequently, equation (27) is specified in Model Three as the domestic demand, replacing equation (10?) in Model Two. A similar specification is made for the export demand equation with identical upward and vertical shift in demand as a result of foreign consumers? increased demand for quality peanut from Ghana (see equation (28) below). 55 The two ordinary demand relationships (i.e. equations (27) and (28)) indicate that the quantity of peanut sold to consumers, both in the domestic and export markets, is not only determined by price but also quality (translated through the shift parameter). Model Three is presented in displaced form as follows: g4666uni0032uni0037g4667uni0009uni0009uni0009uni0009uni0009g1843g3005uni2217 g3404uni0009uni0009g2015g3005g4666g1842g3005uni2217 g3398g2012g4667uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2015g3005 g3407uni0030uni0009g1853g1866g1856uni0009g2012 g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1856g1857g1865g1853g1866g1856g4669 g4666uni0032uni0038g4667uni0009uni0009uni0009uni0009g1843g3025uni2217 g3404uni0009uni0009g2015g3025g4666g1842g3025uni2217 g3398g2012g4667uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2015g3025 g3407uni0030uni0009g1853g1866g1856uni0009g2012 g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1857g1876g1868g1867g1870g1872uni0009g1856g1857g1865g1853g1866g1856g4669 g4666uni0032uni0039g4667uni0009uni0009uni0009uni0009uni0009g1843g3020uni2217 g3404uni0009g2013g3020g1842g3020uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1875g1860g1857g1870g1857uni0009uni0009g2013g3020 g3410uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1856g1867g1865g1857g1871g1872g1861g1855uni0009g1871g1873g1868g1868g1864g1877g4669 g4666uni0033uni0030g4667uni0009uni0009uni0009uni0009uni0009g1842g3005uni2217 g3404uni0009g2009g1842g3020uni2217 g3397g2010uni2032uni0009uni0009g1875g1860g1857g1870g1857uni0009g2009 g3408uni0030uni0009g1853g1866g1856uni0009g2010uni2032g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1868g1870g1861g1855g1857uni0009g1864g1861g1866g1863g1853g1859g1857g4669 g4666uni0033uni0031g4667uni0009uni0009uni0009uni0009uni0009uni0009g1842g3005uni2217 g3404uni0009g1842g3025uni2217uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g4668g1864g1853g1875uni0009g1867g1858uni0009g1867g1866g1857uni0009g1868g1870g1861g1855g1857g4669 g4666uni0033uni0032g4667uni0009uni0009uni0009g1843g3020uni2217 g3404g1837g3005g1843g3005uni2217 g3397uni0009g1837g3025g1843g3025uni2217 uni0009uni2236uni0009uni0009uni0009uni0009g1837g3005 g3404g1843g3005g1843 g3020 g3408uni0030uni0009uni003Buni0009g1837g3025 g3404g1843g3025g1843 g3020 g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009g4668g1865g1853g1870g1863g1857g1872uni0009g1855g1864g1857g1853g1870g1861g1866g1859g4669 where g2010uni2032g3404 g3031g3019g3017 g3253 is the proportionate cost of the aflatoxin regulation (or proportional tax). For purposes of comparing the shifts in supply and demand curves owing to the policy intervention, the form of the price wedge equation in Model Two (i.e. equation (13?)) has been rewritten in Model Three (as shown in equation (30)) in order to interpret regulation compliance costs as ad valorem tax on the market.25 For example, using data in Table 2.2, g2010uni2032g3404 g2873g2868uni002Eg2870g2870g2875g2877g2874 ; suggesting that aflatoxin regulation in Ghana imposes a proportionate tax on the peanut industry equal to 6.3%. The remaining equations in the present model, as well as variables and parameters, are defined in Model Two above (see Tables 2.2 and 2.3). With shift parameters on both the supply and demand sides of the market, we proceed to determine the distribution of economic burden and/or gains between peanut suppliers and consumers. 25 Notice that from equation (13?), uni0009g1842 g3005uni2217 g3404uni0009g2009g1842g3020uni2217 g3397g2010g1844uni2217 where g2010 g3404 g3019 g3017g3253 and g1844 uni2217 g3404 g3031g3019 g3019 . Hence, substituting g2010uni0009and g1844uni2217 yields equation (30), uni0009g1842g3005uni2217 g3404uni0009g2009g1842g3020uni2217 g3397g2010g4593 where g2010uni2032g3404 g3031g3019g3017 g3253 . 56 Following the procedure in Model Two, the system of equations is solved by first deriving the implied export supply equation shown below: g4666uni0033uni0033g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1843g3025uni2217 g3404uni0009uni0009g2013g3025g1842g3025uni2217 g3398uni0009g2013g3020g4666g2010 g4593 g3398g2012g4667g3397g2009g1837g3025g2013g3025g2012 g2009g1837g3025 uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g2013g3025 is the export supply elasticity; g2013g3025uni0009uni0009g3404uni0009uni0009g3084g3268g2879g3080g3012g3253g3086g3253g3080g3012 g3273 uni0009g3408uni0030 Next, the market clearing condition is applied in the export market and all reduced-form relationships are subsequently developed in the following subsection. 2.4.3.1 Economic Incidence Relationships from Model Three The relevant reduced-form equations representing price and quantity incidence of aflatoxin interventions (with simultaneous shifts in peanut supply and demand) are provided below. For consumer prices, the impact of the policy intervention is as follows: g4666uni0033uni0034g4667uni0009uni0009uni0009uni0009g1842g3025uni2217 g3404uni0009g1842g3005uni2217 g3404uni0009 g2013g3020g2010 g4593 g3398g2009g2015uni2032g2012 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667uni0009g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g2015uni2032g3404uni0009g1837g3005g2015g3005 g3397g1837g3025g2015g3025 g3407uni0030 ; g2015uni2032 is the composite demand elasticity facing peanut suppliers in Ghana. Given the small exporter status used in the model, the form of equation (34) changes to the following equation: 26 g4666uni0033uni0034uni2032g4667uni0009uni0009uni0009uni0009g1842g3025uni2217 g3404uni0009g1842g3005uni2217 g3404uni0009g2012uni0009g3408uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 The incidence relationship presented in equation (34) (and (34?)) shows that retail prices for peanut would go up following enforcement of the aflatoxin policy. This analytical result is consistent with those obtained in the previous models. Although the two shift parameters are important in determining the degree of increase in the 26 Employing L'H?pital's Rule, the numerator and denominator of equation (34) above are separately differentiated with respect to g2015g3025 noting that g2015uni2032g3404uni0009g1837g3005g2015g3005 g3397g1837g3025g2015g3025. 57 domestic and export demand prices, these cost and quality changes have counter- effects on the economic welfare of consumers. Precisely, compliance cost from the policy is expected to be welfare decreasing to consumers, whereas quality assurance would be beneficial. However, the effect of the aflatoxin regulation on producer prices (to be received by peanut suppliers) is indeterminate as shown in the following incidence relationship: g4666uni0033uni0035g4667uni0009uni0009uni0009g1842g3020uni2217 g3404 g2015uni2032g4666g2010 g4593 g3398g2012g4667 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667uni0009uni2277uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Thus, since the expression in the denominator is positive, the direction of impact from the policy intervention depends critically on relative magnitudes of the two shift parameters representing compliance cost (g2010g4593), and quality peanut (g2012). To illustrate, if the proportionate shifts in supply and demand curves are such that g2010g4593 g3408uni0009g2012 (e.g. 6% versus 5%, respectively) then one would expect producer prices to be depressed as a result of the policy. The foregoing scenario means that suppliers face a net loss since the decrease in producer price due to compliance costs outweighs the rise in price owing to increased demand for quality peanut. Conversely, supply prices would increase (i.e. net gain to producers) following the introduction of the policy, if g2010g4593 g3407uni0009g2012 (e.g. 6% versus 10%, respectively). Furthermore, if the shift in supply happens to be exactly identical to that of demand (i.e. g2010g4593 g3404uni0009g2012) then we would expect peanut suppliers to be unaffected by the policy since losses from the regulation cost would be nullified by gains from the quality-induced increase in demand. With Ghana as a small exporter in the world peanut market facing a perfectly elastic demand (where g2015g3025 g3404g3398uni221Euni0009g1853g1866g1856uni0009g2015g4593 g3404g3398uni221E), the policy incidence provided in equation (35) reduces to the following relationship: 58 g4666uni0033uni0035uni2032g4667uni0009uni0009uni0009g1842g3020uni2217 g3404g4666g2010 g4593 g3398g2012g4667 g3398g2009 uni0009uni2277uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Having shown that peanut suppliers in Ghana bear the full burden of the aflatoxin policy, as demonstrated in Model Two and Figure 2.3, it is noteworthy that equations (35) and (35?) provide conditions that can mitigate the regulation?s impact on producers. Specifically, it is possible to derive the break-even demand shift. That is, given compliance costs, we can determine the demand shift that would render the regulation costless to peanut producers by setting equations (35) or (35?) to zero. In the preceding case, the quality-induced increase in supply price would just offset the cost-induced decrease in supply price. Furthermore, the remaining incidence relationships shown below demonstrate impacts of the policy on quantities of peanut in the market: g4666uni0033uni0036g4667uni0009uni0009uni0009g1843g3005uni2217 g3404uni0009 g2013g3020g2015g3005g4666g2010 g4593 g3398g2012g4667 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667uni0009uni2277uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0033uni0037g4667uni0009uni0009uni0009g1843g3025uni2217 g3404uni0009 g2013g3020g2015g3025g4666g2010 g4593 g3398g2012g4667 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667uni0009uni2277uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0033uni0038g4667uni0009uni0009uni0009g1843g3020uni2217 g3404 g2013g3020g2015uni2032g4666g2010 g4593 g3398g2012g4667 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667uni0009uni2277uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Thus, effects of the policy intervention on quantities supplied and demanded in the domestic and export markets are indeterminate. Evaluating equations (36) through (38) with perfectly elastic export demand elasticity yields the following relationships: g4666uni0033uni0036uni2032g4667uni0009uni0009uni0009g1843g3005uni2217 g3404uni0009uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0033uni0037uni2032g4667uni0009uni0009uni0009g1843g3025uni2217 g3404uni0009g2013g3020g4666g2010 g4593 g3398g2012g4667 g3398g2009g1837g3025 uni0009uni2277uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0033uni0038uni2032g4667uni0009uni0009uni0009g1843g3020uni2217 g3404g2013g3020g4666g2010 g4593 g3398g2012g4667 g3398g2009 uni0009uni2277uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Equations (36) through (38), except for (36?), indicate that net impacts of the regulation policy are crucially determined by relative sizes of the cost and quality 59 factors (i.e. the two shift parameters in the model). 2.5 Data and Sources The sources of information used in this chapter, including parameters, are mentioned in this section. Data on both shelled and in-shell peanuts are used. Annual trade value and quantities for Ghana was obtained from FAOSTAT (2011) database. Domestic quantity of peanuts demanded was computed by deducting total exports (i.e. shelled and in-shell plus oil) from total production. The quantity of peanut oil exported was converted into unshelled peanuts using a conversion ratio of 3 tonnes of unshelled to 1 tonne of oil (Pattee and Young, 1982; Diop, Beghin and Sewadeh, 2004). Unit prices of Ghanaian peanut were derived from the reported trade value and quantity data. Aflatoxin compliance costs are obtained from Amoako-Attah et al. (2007). The authors estimate costs associated with four alternative post-harvest handling methods for peanut before storage. After carrying out the various drying techniques, the authors recorded the corresponding costs, as well as the aflatoxin contamination levels. The study by Amoako-Attah et al. (2007) was conducted in different locations and seasons in Ghana. Consequently, the authors recommended two best practices for drying peanut with regard to minimizing aflatoxin contamination after harvest. Therefore, this chapter employs cost estimates for the best drying practices provided in Amoako-Attah et al. (2007). Beghin and Matthey (2003) present domestic peanut supply and demand elasticities for selected peanut-producing African countries, especially in the West 60 African sub-region. The reported elasticity values ?? identical for the listed African countries ?? are used for Ghana. Also, United States? export demand elasticity (-1.9), estimated by Boonsaeng, Fletcher and Carpio (2008), is used in a sensitivity analysis where Ghana?s supply to the world market becomes comparable to existing large exporters.27 The study period is 1995 through 2008. 2.6 Results and Discussion This section presents simulation results obtained after applying empirical data to the comparative statics equations (i.e. the reduced-form relationships) derived earlier. Specifically, Table 2.4 exhibits reduced-form elasticities from the first two models together with results from sensitivity analyses. Similarly, results for Model Three are provided in Table 2.5. Finally, price and quantity effects from Models One and Two are applied to estimate changes in economic surplus due to aflatoxin regulation. Thus, attendant welfare for producers and consumers are displayed in Table 2.6. 27 Note that Ghana is currently a small peanut exporter and, therefore, faces a perfectly elastic export demand curve (i.e. |g2751X|=?). This is a major assumption in this chapter and hence it is used as the baseline case. 61 Table 2.4. Percentage Changes in Ghana?s Peanut Prices and Quantities, 1995-2008 Exogenous variable: percentage change in regulation compliance tax (R*) Variables Model 1 Model 1Aa Model 2 Model 2Ab PD* 0.0439 0.0000 0.0000 0.0428 PX* ? ? 0.0000 0.0428 PS* -0.0251 -0.0823 -0.0823 -0.0265 QD* -0.0088 0.0000 0.0000 -0.0086 QX* ? ? 0.0000 -0.0813 QS* -0.0088 0.0000 -0.0288 -0.0093 aSensitivity analysis: in the short run, domestic peanut supply is assumed to be perfectly inelastic (i.e. g2239S=0). bSensitivity analysis for the case where Ghana becomes a larger open economy. Here, export demand is not perfectly elastic (i.e. |g2751X |=1.9 ? ?' = ? ?' < ? PD* 5.0000 6.3100 10.0000 PX* 5.0000 6.3100 10.0000 PS* -1.7100 0.0000 4.8000 QD* 0.0000 0.0000 0.0000 QX* -59.8300 0.0000 168.5400 QS* -0.5983 0.0000 1.6900 Note: ?' (i.e. the regulation compliance cost) is identical in all three cases, whereas ? (i.e. upward demand shift) is assumed to be 5%, 6.31%, and 10%, respectively for Cases 1, 2, and 3. From Case 1, if the regulation imposes 6.3% compliance tax on the peanut industry, then both domestic and export consumer prices face a net increase of 5%, given that the market experiences a 5% upward shift in demand owing to quality assurance from the policy. That is, the proportionate rise in consumer prices is solely due to the increase in demand for peanut with reduced aflatoxin contamination, guaranteed by the regulation. 64 However, the price received by peanut suppliers experiences a net decrease of 1.7%. Notice that in the present scenario, the regulation compliance cost exceeds the rise in demand. Since the overall demand for peanut in Ghana is perfectly elastic, the suppliers pay the full cost of the policy, although proceeds from the high demand for quality peanut helps to ameliorate the economic burden on producers. Similarly, the quantities of peanut supplied domestically and abroad also go down following the pronounced effect of the aflatoxin cost parameter. However, quantity demanded in the domestic market is unaffected by the policy, an observation common to all the three cases shown in Table 2.5. For Case 2, where the proportionate increase in demand equals that of the regulation compliance cost, it is observed that the aflatoxin policy would be costless to peanut suppliers. To the peanut suppliers, the quality-induced increase in supply price is exactly identical to the cost-induced decrease in that same price. Therefore, the present scenario demonstrates the break-even demand shift, where the increase in retail price fully defrays the reduction in producer price. Consequently, quantities of peanut in the domestic and export markets are completely unaffected. In Case 3, where the quality-induced demand shift (10%) exceeds the cost- induced supply shift (6.31%), we observe that producers experience a net gain from the regulation even though they bear the entire cost of the policy. Specifically, if the aflatoxin intervention introduces a 6.31% compliance cost into the industry, then a 10% increase in demand for quality peanut would generate the following price and quantity effects: retail prices increase by 10% (proportional to the vertical shift in demand); supply price increases by 4.8%; quantity of peanut produced will increase by 1.7%; quantity traded in the export market rises by 168.5%; and quantity demanded in the domestic market remains unchanged. 65 In the present scenario, the gain in price received by producers owing to the enhanced demand for quality peanut outweighs the reduction in that supply price as a result of regulation compliance costs. 2.6.1 Approximated Welfare Implications: Model One versus Model Two Finally, estimated economic welfare changes triggered by a 10% rise in standards compliance costs are shown in Table 2.6. This scenario is akin to the introduction and enforcement of strict aflatoxin standards. Notice that the quality improvement effects of the aflatoxin policy are suppressed in the welfare estimates shown in Table 2.6 below. Table 2.6. Welfare Changes (million US$) Induced by 10% Rise in Compliance Cost in Ghana Results Autarkic Case Small-Open-Economy Case PS CS PS CSD CSX Baseline -47.4020 -105.3864 -137.0000 0.0000 0.0000 Sensitivity Analysis -160.0000 0.0000 -49.8505 -101.8861 -0.5792 Note: PS and CS mean ?Producer Surplus? and ?Consumer Surplus?, respectively. The superscripts D and X, respectively, denote ?Domestic? and ?Export?. Also, Autarkic Case refers to Model One while Small-Open-Economy Case represents Model Two. Ignoring possible demand enhancement due to the regulation, results in Table 2.6 show that increases in costs associated with aflatoxin minimization are largely welfare decreasing, regardless of trade status. Where the Ghanaian peanut market is entirely domestic, producers and consumers lose over US$ 47 million and US$ 105 million, respectively. However, peanut suppliers experience economic welfare loss of US$ 160 million in the short-run, with consumers completely unscathed. 66 In addition, accounting for cross-border trade with Ghana as a small exporter in the world market (i.e. Model Two which ignores potential increase in demand for quality peanut) reveals a lopsided welfare loss of US$ 137 million for producers. Thus, peanut suppliers pay the full cost of complying with the aflatoxin regulation imposed on the industry. On the other hand, a scenario change analysis indicates that if Ghana were a large exporter then producers would be less impacted than consumers. Precisely, producers lose about US$ 50 million, while domestic and export consumers experience welfare reductions in excess of US$ 100 million and US$ 0.5 million, respectively. 2.7 Summary and Concluding Remarks The primary objective of this chapter was to evaluate the distribution of economic impacts generated by enforcement of aflatoxin regulation on the peanut sector in Ghana, after incorporating trade status and potential increase in demand due to quality assurance from the intervention. By implication, the paper sought to bring to the fore any possible economic welfare incidence on domestic market participants following the enforcement of aflatoxin standards. To achieve that goal, the Equilibrium Displacement Modeling technique was employed. The economic framework was presented in three nested model scenarios, namely an autarkic peanut sector, a small exporter with supply shift only, and a small exporter with simultaneous shifts in supply and demand. Data covering the period 1995 through 2008 was obtained from FAO Statistics/database in addition to other sources cited in the text. 67 In the autarkic model, results suggest that increases in aflatoxin compliance cost causes producer price of peanut to fall, and raises consumer prices. Hence, both producers and consumers share the cost of the aflatoxin policy intervention in Ghana. However, a comparison of the size of price effects due to the regulation indicates that peanut consumers are impacted more than producers. Consumers are more responsive to the policy since domestic demand is less elastic than supply (in absolute terms). In other words, consumers being the less elastic side of the peanut market accordingly bear the greater economic incidence of the aflatoxin policy. Moreover, price and quantity effects of the policy translate into changes in economic welfare on the part of market participants. Hence, according to the autarkic model, approximated welfare effects generally indicate losses for both producers and consumers in Ghana. On the other hand, opening up the peanut economy to cross-border trade, without accounting for improvements in peanut quality, shows that producers bear the entire economic burden, whereas domestic and export consumer prices (and quantities demanded) are unaffected by the policy intervention. In the present model scenario, peanut producers bear the full incidence of the policy because the overall demand they face is perfectly elastic due to Ghana?s small trade status. In spite of peanut producers bearing the full cost of the aflatoxin policy (as a result of Ghana?s small export trade), this study also shows that if demand for quality peanut is accounted for in the policy evaluation then it is possible that the adverse effects on suppliers could be mitigated, and that producers could even benefit from the intervention. Thus, incorporating demand for quality enhancement in peanut following the aflatoxin regulation policy, the incidence analyses conducted in this chapter reveal interesting results of policy relevance. Precisely, if the induced shift in demand owing to quality assurance exceeds the induced shift in supply due to 68 aflatoxin compliance cost, then suppliers would gain from the policy intervention even though they bear the entire cost of the regulation due to Ghana?s status as a small peanut exporter. Overall, the government of Ghana may consider trade status, as well as aflatoxin-free food promotions (i.e. to raise awareness among economic agents regarding the aflatoxin problem) as important policy instruments to be employed in order to alleviate any economic burden on the population, following enforcement of aflatoxin regulations. 69 Chapter 3: Willingness to Pay for Safer Foods: Consumer Preference for Aflatoxin-free Peanut in Ghana 3.1 Problem Statement In tandem with the broad objective of determining aflatoxin policy implications on the economic welfare of food market participants, it is important to assess how consumers value peanut with reduced aflatoxin contamination (considered safe for consumption). Such knowledge would be useful to scientists and policymakers in the evaluation of aflatoxin interventions. Moreover, understanding peanut consumers? willingness to pay for aflatoxin-free peanut is critical to the success and sustainability of efforts to reduce aflatoxin (and other mycotoxins) contamination. Findings from the first two chapters of this dissertation generally indicate that aflatoxin regulation may result in economic losses to both producers and consumers since peanut prices and quantities are negatively impacted. From Chapter One in particular, compliance costs in peanut supply chains (following aflatoxin standards enforcement) have been shown to lead to rising retail prices often interpreted as welfare decreasing to consumers. However, the first dissertation chapter is limited in the sense that it ignores possible rise in demand for aflatoxin-free peanut due to safety assurance from the aflatoxin policy intervention. In addition, analyses performed in Chapter Two reveal that economic losses from the policy are ameliorated (and, in fact, gains are sometimes recorded) when consumer demand for quality peanut is appropriately taken into account in the evaluation. 70 However, the foregoing conclusion is strongly based on the assumption that consumers would be willing to pay for quality assurance from the regulation policy. That is, there is no empirical evidence of consumer preference and willingness to pay (WTP) for peanut with reduced aflatoxin contamination. Therefore, the present study contributes to knowledge in that it focuses on consumers? willingness to pay price premiums for quality (or safer) peanut assured from governments? enforcement of aflatoxin standards. 3.2 Objectives This chapter accounts for quality-improvement effects of aflatoxin regulation in Ghana by studying consumers? valuation of peanut with reduced contamination. Specific objectives of the present research are as follows: (1) To evaluate consumers? familiarity or awareness of aflatoxin contamination and other food contaminants. (2) To determine whether consumers in Ghana are willing to pay more (i.e. price premium) for the supply of aflatoxin-free peanut. (3) To isolate some important socioeconomic characteristics of consumers in Ghana that may influence their willingness to pay for aflatoxin-free peanut. 71 3.3 Related Literature This section reviews relevant studies in the contingent valuation (CV) literature. First, emphasis is placed on existing research with regard to consumers? stated and revealed preferences for safer food products; using CV surveys. Next, we highlight relevant features/methods in the CV literature aimed at improving the realization of valid WTP estimates. 3.3.1 Importance of Food Safety to Consumers Using the best-worst scaling method, Lusk and Briggeman (2009) investigate the stability of consumer preferences for a set of food values. The authors found that ?safety? was among the most important food attributes. Food safety was also shown to be related to people?s stated and revealed preferences. Wang, Mao, and Gale (2008) carried out a CV survey in China concerning consumer interest in food safety issues. Report from their study reveals that consumers are willing to pay price premiums for milk products certified under the Hazard Analysis Critical Control Point (HACCP). The authors employed a hedonic price model to analyze their survey data. In Taiwan, Jan, Fu, and Huang (2005) estimate consumers? demand and WTP for safer hypothetical cigarettes known to reduce lung cancer risk. The authors conducted a contingent valuation survey on 264 smokers and subsequently employed a dichotomous-choice model in a random utility framework. Jan, Fu, and Huang found that consumers were willing to pay an average price premium of 152% relative to existing market prices. The authors argue that the high WTP values indicate people?s demand for healthy products. 72 In spite of the high stated preference for safe cigarettes, the authors acknowledge that the study?s findings may be limited due to its small sample size. A study by Eom (1994) also shows that consumers in the United States are willing to pay high price premiums to avoid adverse health issues associated with pesticide residues in food. Eom (1994) integrates important concepts on food safety, namely ?perceptions, behavior, and valuation?, in a random utility framework. Individuals? stated preferences were estimated using discrete choice models. The literature generally suggests that people are concerned about food safety and are, therefore, willing to pay more for safer food products and services. 3.3.2 Hypothetical Bias in Contingent Valuation Studies One of the important methodological challenges in the application of CV surveys is minimizing ?hypothetical bias?; defined as the difference between people?s WTP in hypothetical markets (where products are hypothetical and money is not involved) as opposed to experimental market settings where real products and money transactions occur (see Cummings and Taylor, 1999; List and Gallet, 2001; Alfnes, Yue, and Jensen. 2010). List and Gallet (2001) conducted meta-analyses to identify factors that affect hypothetical bias in WTP values. They indicate, among others, that the problem of hypothetical bias is ?systematically? less prevalent in WTP as against willingness-to- accept (WTA) surveys. Also, the authors show that hypothetical bias occurs more frequently in CV studies involving public goods than with private goods, even though Murphy et al. (2005) found ?weak evidence? in support of that claim. Furthermore, Whitehead et al. (1995) argue that the ?validity and reliability? of WTP values obtained from CV surveys are enhanced when participants are familiar with 73 the goods and services in question; as opposed to the case where respondents are not used to the product/service. Therefore, the hypothetical CV survey discussed in this chapter is appropriate given that peanut is a private good and an important food product in Ghana. 3.3.3 Use of Double-bounded Dichotomous Choice Models Discrete-choice models have been widely applied in the analyses of numerous CV surveys. Double-bounded dichotomous choice models are known to perform better than the single-bounded dichotomous choice alternative, in terms of providing more efficient WTP estimates (Hanemann, Loomis, and Kanninen, 1991; Kanninen, 1993; McCluskey et al., 2003). This subsection briefly highlights some selected studies that have employed double-bounded dichotomous choice methods to evaluate a number of contingent valuation problems. With the application of a standard double-bounded dichotomous choice model on CV data, Lin et al. (2005) evaluate consumers? WTP for biotech rice and soybean oil in China. Findings suggest that people in China prefer non-biotech foods to biotech products ?? consumers are willing to pay high premiums for non-biotech foods. The stated WTP for non-biotech rice is between 41.5% and 74%. Similarly, WTP for non-biotech soybean oil ranges from 23.4% to 52.6%. The authors argue that food safety considerations influence consumers? WTP since the stated price premiums for rice, an important food staple, appear substantial than soybean oil. Notwithstanding the key role played by food safety fears, the authors partly attribute the high price premium to possible hypothetical bias from the CV survey. 74 McCluskey et al. (2003) analyze consumer preference for genetically modified (GM) foods in Japan. The authors applied a semi-double-bounded dichotomous choice model on their contingent valuation survey data. Results indicate that 80% of respondents were not willing to accept GM foods even with price discounts. McCluskey et al., therefore, show that consumer behavior is influenced by food safety concerns. De Groote and Kimenju (2008b) investigate Kenyan?s preference for yellow (biofortified) maize versus white maize. The authors applied the semi-double- bounded dichotomous choice method on contingent valuation survey data collected on urban consumers. Although standard white maize is often deficient in vitamin A, the authors indicate that people in Kenya consider biofortified yellow maize as inferior to white maize. De Groote and Kimenju, therefore, ignored the possibility of price premiums and concentrated on consumer acceptance of yellow maize, with and without discounts. Hence, their study had three WTP response categories. De Groote and Kimenju (2008b) show that urban consumers exhibited strong preference for white maize and would only buy yellow/biofortified maize on discounts. However, there was some interest in fortified maize meal although price premiums were modest, ranging from 6% to 7.4%. In addition, Kimenju and De Groote (2008a) explore how consumer willingness to pay for genetically modified food is determined by awareness, perceptions, and socioeconomic characteristics. The authors employ a standard double-bounded dichotomous choice model and find that even though most people in Kenya accept GM foods their willingness to pay is negatively affected by safety concerns. 75 The findings are consistent with studies conducted in other parts of the world regarding the importance of food safety and health considerations in consumer decisions. 3.3.4 Addition to the Food Safety Discussion As discussed earlier, the literature on consumers? willingness to pay premiums (or accept discounts) for consumption goods or services is quite extensive. Food safety and environmental concerns have largely motivated discussions in published studies. On food safety, the existing research on consumer preferences only highlight acceptance of genetically modified foods, as well as consumer interests in the reduction of chemical residues in food products. To the best of my knowledge, the CV literature provides no information on consumer behavior toward the mycotoxin problem; in spite of the predominant role of these toxins in global food safety concerns. Therefore, it is important to study consumer awareness and willingness to pay for food products with reduced aflatoxin contamination. 3.4 Theoretical/Conceptual Framework Analyses in this study are based on the random utility theory predominantly applied in contingent valuation problems. Using consumer theory, this research invokes the key assumption that individuals make choices to maximize their utility in the face of limited budgets (Hanemann and Kanninen, 1998; Lusk and Hudson, 2004; De Groote and Kimenju, 2008b; Gallardo et al. 2009). That is, the central goal of this chapter is to study and understand the importance of quality food products to consumer utility. 76 This is achieved below through the assessment of individuals? stated preferences for peanut with reduced aflatoxin content. 3.4.1 Contingent Valuation Survey in Ghana The data used in this chapter were collected in a survey carried out in Ghana from May through July, 2012. Contingent valuation (CV) questionnaires were used in face- to-face interviews with peanut consumers who agreed to participate in the survey (see Appendix 3 for questionnaire, and interview guide). In CV methods, researchers conduct surveys on subjects sampled from target populations and elicit their willingness to pay more (price premium) or accept compensation (price discount) for a proposed change in products/services. Individuals? willingness to pay (WTP) for a given change is determined in hypothetical market settings using survey instruments such as questionnaires; with interactions through mails, telephones, or face-to-face interviews. In the present research, survey design and questionnaire administration were carefully executed in accordance with recommended practices in the CV literature (Portney, 1994; Carson et al., 2003; McCluskey et al., 2003; Gallardo et al., 2009). For instance, interviewers explained to respondents that researchers have found strong evidence of the association between aflatoxin exposure and health problems, namely aflatoxicosis, immune system suppression, liver cancer, among others (Wang et al., 2001). In view of the negative health issues associated with dietary aflatoxin exposure, survey participants were briefed on potential benefits of consuming peanut with zero or reduced contamination. Furthermore, the referendum format of value elicitation was adopted in that respondents were offered the opportunity to vote either 77 in favor or against aflatoxin policy interventions that would ensure availability of safer peanuts in markets but at higher prices. Consumers who vote in favor of regulation enforcement are subsequently asked to state the premium they are willing to pay for aflatoxin-free peanuts. Thus, information on respondents WTP was solicited using both referendum voting and open-ended questions where consumers indicate precisely how much they are willing to pay relative to existing local market prices (reference points). Since peanut is an important food crop consumed in various forms in Ghana, the use of CV methodology is legitimate. Wedgwood and Sansom (2003 p.7) argue that ?when the CV method is used to estimate the use of goods and services with which the individuals are familiar...CV surveys that are carefully designed and administered can yield accurate and useful information on household preferences (Cummings et al, 1986).? A sample of 652 peanut consumers was randomly selected to participate in the survey, after pre-testing the questionnaire on 30 consumers in Kumasi. Survey participants were sampled from five (out of ten) administrative regions of Ghana. The purposively selected regions are Ashanti, Brong Ahafo, Western, Central, and Eastern (see Table 3.1). Capital cities of the listed regions were selected since urban centers are prominent destination markets for peanut produced in the northern part of the country. Table 3.1 shows the proportional samples of consumers in the selected regions according to population size. The overall sample size was drawn from the administrative regions such that regions with larger populations contributed many survey participants compared to the smaller ones. This proportional sampling was adopted to reflect the importance of large regions as major peanut markets. 78 Table 3.1. Selected Regions in Ghana and Sample Sizes Region Population* Sample Size Capital City Ashanti 4,780,380 299 Kumasi Eastern 2,633,154 109 Koforidua Western 2,376,021 92 Takoradi Brong Ahafo 2,310,983 86 Sunyani Central 2,201,863 66 Cape Coast Total Sample Size 652 *Population figures obtained from Ghana Statistical Service (2012). Various sub-metropolitan areas within each capital city were identified and peanut consumers chosen from those areas. A total of 68 areas (referred to as ?suburbs?) were covered. The sampling procedure for choosing peanut consumers was systematic where every third individual (representing a household) along a given street was interviewed. In cases where the selected individual failed to qualify as a respondent, the interviewers moved to the next person and repeated the sampling order after successfully identifying a peanut consumer. Figure 3.1 shows the geographical distribution of the survey regions and corresponding urban centers. 79 Figure 3.1. Map of Ghana Showing Distribution of Regions and Urban Centers Source: adapted from Owusu (2005). The survey was approved by the Auburn University Institutional Review Board. Furthermore, before proceeding with the survey, the interviewers sought the approval of participants after reading out consent protocols to them. 80 The questionnaires were administered by trained interviewers in the face-to-face interviews conducted with peanut consumers who agreed to participate in the survey. It is worth emphasizing that interviewers explained to respondents the goal of the survey and also provided concise description of the peanut aflatoxin issue with possible regulation enforcement in the future. In the course of the interviews, respondents were shown printed photographs of three peanut samples labelled as follows: ?Sample A?, ?Sample B?, and ?Sample C? where ?C? was a clean and well- sorted peanut sample with no moldy, broken or shriveled kernels whereas ?A? was a sample with high proportion of moldy, broken and shriveled kernels plus other foreign materials; Sample B was moderately sorted peanuts with lower percentage of broken/shriveled kernels. Thus, Sample A would typically possess the highest possibility of aflatoxin contamination while Sample C would have the least contamination among the three, and therefore, the safest product. Respondents were then asked to make their choices and state whether they would vote for aflatoxin regulation that will ensure availability of aflatoxin-free peanuts in local markets (such as Sample C), and most likely result in increased prices. Consumers who indicated their willingness to pay were subsequently asked to state how much they would be willing to pay for aflatoxin-free peanuts. Respondents were frequently prompted to make objective choices (or take decisions) in the context of their peculiar preferences, limited income, and food expenditure patterns. 3.4.2 Methods of Estimating Willingness to Pay There are two main methods commonly used to elicit people?s WTP, namely the application of single-bounded dichotomous-choice approach, or the use of double- 81 bounded dichotomous-choice procedures. However, the double-bounded dichotomous-choice method has been the preferred approach over the past two decades due to its desirable property of yielding more efficient WTP estimates (Hanemann, Loomis, and Kanninen, 1991; Kanninen, 1993; McCluskey et al., 2003). The double-bounded dichotomous-choice method introduces an additional dichotomous-choice question in order to obtain more reliable results. Since the double-bounded dichotomous-choice technique is a generalized version of the single- bounded dichotomous-choice method, we first discuss the latter, and subsequently introduce the former and its variant form which is the focus of this chapter. This subsection adapts theoretical derivations in De Groote and Kimenju (2008a, 2008b). 3.4.2.1 Single-Bounded Dichotomous-Choice Method The random utility model is operationalized in dichotomous-choice contingent valuation functions as shown below. Although consumers are assumed to know their preferences with certainty, investigators and econometricians perceive individual utility functions as consisting of systematic and random or unobservable components (Hanemann, 1984; Hanemann and Kanninen, 1998). To the investigator, therefore, a given peanut consumer?s utility is stated as follows; g4666uni0031g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1847g3036 g3404uni0009g1858g4666g1877g3036uni002Cg1878g3036uni002Cg1857g3036g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where y is the individual?s income, z is a vector of the respondent?s socioeconomic and/or demographic characteristics, e is the random term and subscript i represents the consumer. Given that consumer utility is directly unobservable to researchers, probability of utility maximization is obtained from individuals? observed behavior. In dichotomous-choice questions, people are required to indicate whether they would agree to pay a proposed price or not. 82 Owing to the utility maximization objective, consumers would be willing to pay for a new product if they believe that the proposed change (such as the introduction of aflatoxin-free peanut) will increase or retain their existing utility (Hanemann, 1984; Hanemann and Kanninen, 1998). The preceding assumption is expressed below in probabilities; g4666uni0032g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3036 g3404uni0009g1842g4666g1847g3036g2869g4666g1877g3036 g3398g1828g3036uni002Cg1878g3036uni002Cg1857g3036g2869g4667g3410uni0009g1847g3036g2868g4666g1877g3036uni002Cg1878g3036uni002Cg1857g3036g2868g4667g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g1842g3036 is the probability of a consumer?s willingness to pay a bid price of g1828g3036 for the new product; g1847g3036g2869 is the final utility after acquiring the new product; g1847g3036g2868uni0009is the initial utility before buying the new product; g1877g3036is the consumer?s income; g1878g3036 is a vector of the individual?s demographic information; and g1857g3036g2869 is the random component after obtaining the new product, while g1857g3036g2868 is the random term for the case without the new product. Notice that the bid price is paid directly from the consumer?s income. Therefore, consumers will agree to pay a bid price when their willingness to pay equals or exceeds the offered price of the aflatoxin-free peanut, otherwise they will reject the bid. This consumer behavior is illustrated in the next two equations: g4666uni0033g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0050uni0072g4666g1840g1867uni0009g1872g1867uni0009g1828g4667g3404uni0009g1842g1870g4666g1828 g3408uni006Duni0061uni0078g1849g1846g1842g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 and g4666uni0034g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0050uni0072g4666g1851g1857g1871uni0009g1872g1867uni0009g1828g4667g3404uni0009g1842g1870g4666g1828 g3409uni006Duni0061uni0078g1849g1846g1842g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 Equation (3) indicates that an individual will reject (or say ?No? to) the supply of aflatoxin-free peanut if the proposed bid price is greater than his maximum willingness to pay. On the other hand, a consumer will accept (or say ?Yes?) to an offer on condition that his maximum willingness to pay outweighs or, at least, is identical to the bid price of the new product. Derivations presented so far imply that consumer willingness to pay for new products depends on bid price, as well as individual/demographic factors. 83 Hence, the distribution of maximum willingness to pay i.e. g1833g4666g1828uni003Buni03B8g4667uni0009is presented as a cumulative distribution function of the bid price (B), and a vector of parameters g2016.uni0009 g8g147g151uni0061g150g139g145g144g149uni0009 (uni0033)uni0009 uni0061g144g134uni0009 (uni0034)uni0009 uni0061uni0072euni0009 uni0072eg149g146eg133g150g139g152eg142g155uni0009 euni0078g146uni0072eg149g149eg134uni0009 g139g144uni0009 g149g151g139g150uni0061g132g142euni0009 g134g139g149g150uni0072g139g132g151g150g139g145g144uni0009 g136g151g144g133g150g139g145g144g149uni0009uni0061g149uni0009g136g145g142g142g145wg149g483uni0009uni0009 (uni0035)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni03C0g2924(uni0042)=uni0009g1842g1870(g1828 g3408uni006Duni0061uni0078g1849g1846g1842)=g1833(g1828uni003Buni03B8)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 and (uni0036)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni03C0g2935(uni0042)=uni0009g1842g1870(g1828 g3409uni006Duni0061uni0078g1849g1846g1842)=uni0031g3398g1833(g1828uni003Buni03B8)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where uni03C0g2924 is the probability of bid rejection, whereas uni03C0g2935 is the probability of a consumer agreeing to pay a bid price. Typically, the logistic distribution is employed. The S-shape of the logistic distribution function with values ranging from 1 to 0 provides the opportunity to estimate the probability of consumers? willingness to pay given a bid price. Consistent with consumer theory, CV studies assume a downward-sloping logistic function in order to represent the decreasing probabilities of consumers? willingness to pay as the bid price increases (see De Groote and Kimenju, 2008b). Thus, assuming the logistic functional form, we can express the two possible outcomes of individuals? willingness to pay, from Equations (5) and (6) respectively, as follows: (uni0037)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni03C0g2924(uni0042)=uni0009g1833(g1828uni003Buni03B8)=uni0031 (uni0031g3397euni0078g146(g3398(g2009g3398g2025g1828)))g3415 uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 (uni0038)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni03C0g2935(uni0042)=uni0031g3398g1833(g1828uni003Buni03B8)=uni0031g3398uni0009uni0031 (uni0031g3397euni0078g146(g3398(g2009g3398g2025g1828)))g3415 uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where (uni0039)uni0009uni0009uni0009uni0009uni0009uni0009g1833(g1828uni003Buni03B8)=uni0031 (uni0031g3397euni0078g146(g3398g1874))g3415 uni0009uni0009 is the cumulative distribution function (cdf) for the logistic distribution; g1874 =(g2009g3398g2025g1828) is an index function assumed to be linear in bid price; and g2009 and g2025 are elements of the parameter vector, uni03B8. It must be emphasized that the sign of g2025 is positive, thereby ensuring a downward-sloping demand curve 84 (i.e. probability of WTP) consistent with economic theory. The corresponding log likelihood function is derived as follows; (uni0031uni0030)uni0009uni0009uni0009uni0009uni0009uni004C(uni03B8)=g3533(g1856g3036g3052 g3015 g3036g2880g2869 g1864g1866uni03C0g2935(g1828g3036)g3397g1856g3036g3041g1864g1866uni03C0g2924(g1828g3036))uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 OR (uni0031uni0030uni2032)uni0009uni004C(uni03B8)=g3533(g1856g3036g3052 g3015 g3036g2880g2869 g142g144uni0009(uni0031g3398g1833(g1828uni003Buni03B8))g3397g1856g3036g3041g1864g1866g1833(g1828uni003Buni03B8))uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g1856g3036g3052 is a binary-indicator variable which equals 1 if the ith respondent accepts the bid price, and 0 otherwise; similarly, g1856g3036g3041 equals 1 if the ith respondent rejects the bid price and 0 otherwise. Estimation of the vector of parameters in the log likelihood function is then achieved using the maximum likelihood estimator. Subsequently, the mean (and median) willingness to pay is derived from the estimated parameters using the following formula: (uni0031uni0031)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1865g1857g1853g1866uni0009g1849g1846g1842=g2009 g2025g3415 uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 3.4.2.2 Double-Bounded Dichotomous-Choice Method Here, derivations for the single-bounded CV case are extended to the double-bounded dichotomous-choice model, where each respondent faces two bid prices with the magnitude of the second price contingent on the individual?s answer to the first price (see De Groote and Kimenju, 2008a, 2008b). That is, each person is offered a first dichotomous-choice question with a proposed price uni0042g2869 and if the individual agrees to pay this price then the interviewer follows up with another dichotomous-choice question with a higher price,uni0009uni0042g2892. However, if the respondent rejects the first bid uni0042g2869 then he is offered a second dichotomous-choice question with a lower bid price uni0042g2896. 85 The double-bounded dichotomous-choice method, therefore, produces four possible outcomes with the following WTP probabilities: (uni0031uni0032)uni0009uni03C0g2935g2935g3435g1828g3036g3022g3439=uni0009g1842g1870g3435g1828g3036g3022 g3409uni0009uni006Duni0061uni0078g1849g1846g1842g3036g3439=uni0031g3398g1833g3435g1828g3036g3022uni003Buni03B8g3439uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 (uni0031uni0033)uni0009uni0009uni03C0g2935g2924g3435g1828g3036g2869uni002Cg1828g3036g3022g3439=uni0009g1842g1870g3435g1828g3036g2869 g3409uni0009uni006Duni0061uni0078g1849g1846g1842g3036 g3409g1828g3036g3022g3439=g1833g3435g1828g3036g3022uni003Buni03B8g3439g3398g1833(g1828g3036g2869uni003Buni03B8)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 (uni0031uni0034)uni0009uni0009uni03C0g2924g2935(g1828g3036g2869uni002Cg1828g3036g3013)=uni0009g1842g1870(g1828g3036g3013 g3409uni0009uni006Duni0061uni0078g1849g1846g1842g3036 g3409g1828g3036g2869)=g1833(g1828g3036g2869uni003Buni03B8)g3398g1833(g1828g3036g3013uni003Buni03B8)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 and (uni0031uni0035)uni0009uni0009uni0009uni0009uni0009uni0009uni03C0g2924g2924(g1828g3036g3013)=uni0009g1842g1870(uni0009g1828g3036g3013 g3408uni006Duni0061uni0078g1849g1846g1842g3036)=g1833(g1828g3036g3013uni003Buni03B8)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where uni03C0g2935g2935 is the probability of a respondent accepting both first and second bid prices; uni03C0g2935g2924 is the probability of a respondent accepting the first bid but rejecting the second price; uni03C0g2924g2935 is the probability of a respondent rejecting the first price but accepting the second price; uni03C0g2924g2924 is the probability of a respondent rejecting both first and second bid prices; g1828g3036g3013 g3407g1828g3036g2869 g3407g1828g3036g3022 and g1833(.) is assumed to be a logistic distribution. The corresponding log likelihood function is; (uni0031uni0036)uni0009uni004C(uni03B8)=g3533(g1856g3036g3052g3052 g3015 g3036g2880g2869 g1864g1866uni03C0g2935g2935g3435g1828g3036g3022g3439g3397g1856g3036g3052g3041g1864g1866uni03C0g2935g2924g3435g1828g3036g2869uni002Cg1828g3036g3022g3439g3397g1856g3036g3041g3052g1864g1866uni03C0g2924g2935(g1828g3036g2869uni002Cg1828g3036g3013) g3397g1856g3036g3041g3041g1864g1866uni03C0g2924g2924(g1828g3036g3013))uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g1856g3036g3052g3052 is a binary-indicator variable which equals 1 if the ith respondent accepts both bids, and 0 otherwise; g1856g3036g3052g3041 equals 1 if the ith respondent accepts the first price but rejects the second bid price and 0 otherwise; g1856g3036g3041g3052 equals 1 if the ith respondent rejects the first price but accepts the second bid and 0 otherwise; and g1856g3036g3041g3041 equals 1 if the ith respondent rejects both prices, and 0 otherwise. The maximum likelihood estimator is then employed to estimate parameters in the log likelihood function, and the mean willingness to pay is derived as in the single- bounded CV case presented earlier. 86 3.4.2.3 Application of Semi Double-Bounded Dichotomous-Choice Method The present study has three WTP (response) categories discussed below. As a result, this chapter estimates a modified version of the double-bounded dichotomous-choice approach. Specifically, we estimate a Semi Double-Bounded (SDB) logistic model ?? a special form of the standard double-bounded logistic method (McCluskey et al., 2003; De Groote and Kimenju, 2008b; Meenakshi et al., 2012). In this dissertation, the SDB model is employed to determine the probability of consumers? willingness to pay for safer peanuts as a function of prices, as well as relevant socioeconomic factors. This research focuses on willingness to pay price premiums for quality peanut, where a consumer?s stated price for aflatoxin-free peanut must exceed the existing peanut price in his local market. As a result, prices in respondents? preferred markets serve as their lower-bound prices. The reduction of aflatoxin levels in peanut is assumed to be product-enhancing. Therefore, we ignore discount prices in this study since aflatoxin-free peanuts would be of superior quality relative to peanut commonly available in Ghanaian local markets. Precisely, the following three response levels are used to measure consumers WTP for peanut with reduced aflatoxin contamination: 1.) ?No? : This means rejection of both first-bid and second-bid prices; 2.) ?Yes?No?: Acceptance of first-bid price but a rejection of a second-bid price; 3.) ?Yes?Yes?: Acceptance of both first-bid and second-bid prices; where the first-bid price refers to the initial stated price that strictly exceeds existing price in a respondent?s preferred market, whereas the second-bid price is a respondent?s next stated price (following acceptance of the first-bid price) that must necessarily be greater than his previously stated price. 87 This implies that a respondent who rejects the first-bid price would not be willing to pay any premium for aflatoxin-free peanuts. In this study, if a consumer agrees to the first bid g1828g3036g2869uni002C he is subsequently asked for a second higher bid g1828g3036g3022. However, if the respondent answers ?No? to the first bid then that terminates the elicitation process. Therefore, following the procedure and assumptions invoked for the two dichotomous-choice methods derived earlier, the corresponding probabilities for all three WTP-response categories in this study are presented as follows: (uni0031uni0037)uni0009uni0009uni0009uni0009uni0009uni03C0g2924(g1828g3036g2869)=uni0009g1842g1870(g1828g3036g2869 g3408uni006Duni0061uni0078g1849g1846g1842g3036)=g1833(g1828g3036g2869uni003Buni03B8)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 (uni0031uni0038)uni0009uni0009uni0009uni0009uni0009uni0009uni03C0g2935g2924g3435g1828g3036g2869uni002Cg1828g3036g3022g3439=uni0009g1842g1870g3435g1828g3036g2869 g3409uni0009uni006Duni0061uni0078g1849g1846g1842g3036 g3409g1828g3036g3022g3439=g1833g3435g1828g3036g3022uni003Buni03B8g3439g3398g1833(g1828g3036g2869uni003Buni03B8)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 and (uni0031uni0039)uni0009uni0009uni0009uni0009uni0009uni03C0g2935g2935g3435g1828g3036g3022g3439=uni0009g1842g1870g3435g1828g3036g3022 g3409uni0009uni006Duni0061uni0078g1849g1846g1842g3036g3439=uni0031g3398g1833g3435g1828g3036g3022uni003Buni03B8g3439uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where uni03C0g2935g2935 is the probability of a respondent accepting both first and second bid prices; uni03C0g2935g2924 is the probability of a respondent accepting the first bid but rejecting the second price; uni03C0g2924 is the probability of a respondent rejecting the first price and, by implication, the second bid price; the WTP probabilities and bid prices respectively have the following order, uni03C0g2935g2935 g3408uni03C0g2935g2924 g3408uni03C0g2924 and g1828g3036g3022 g3408g1828g3036g2869; and g1833(.) is the cumulative distribution function for the logistic distribution. Equation (17) shows the probability of consumers who would not be willing to pay a price premium for aflatoxin-free peanuts. That is, their maximum WTP are lower than bids that exceed prevailing prices in their preferred markets. In Equation (18), the probability of a consumer offering a price premium but declining to further increase the premium in a follow-up question suggests that his maximum WTP falls between his stated price and a higher bid. Finally, from Equation (19), we note that the probability of a consumer agreeing to pay a premium through his stated first and second bid prices indicates that his maximum WTP is above the highest bid he offers 88 to pay. With the WTP probabilities specified, the corresponding log likelihood function is shown below: (uni0032uni0030)uni0009uni0009uni0009uni004C(uni03B8)=g3533(g1856g3036g3052g3052 g3015 g3036g2880g2869 g1864g1866uni03C0g2935g2935g3435g1828g3036g3022g3439g3397g1856g3036g3052g3041g1864g1866uni03C0g2935g2924g3435g1828g3036g2869uni002Cg1828g3036g3022g3439g3397g1856g3036g3041g1864g1866uni03C0g2924(g1828g3036g2869))uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g1856g3036g3052g3052 is a binary-indicator variable which equals 1 if the ith consumer accepts both bids, and 0 otherwise; g1856g3036g3052g3041 equals 1 if the ith consumer accepts the first price but rejects the second bid price, and 0 otherwise; and g1856g3036g3041 equals 1 if the ith consumer rejects both prices, and 0 otherwise. Similarly, the maximum likelihood estimator is employed to estimate parameters in the log likelihood function. Also, the median WTP can be computed as shown in Equation (11) after estimating a simple polytomous ordered logistic regression; where the WTP categories are regressed on maximum bid prices stated by the respondents. In addition to mean WTP, this study estimates the impact of socioeconomic and demographic characteristics on individuals? willingness to pay for aflatoxin-free peanuts. This is achieved by augmenting the model?s index function through the inclusion of important factors that may influence consumers WTP. Thus, the probabilities of respondents? WTP for safer peanuts would depend on bid prices as well as relevant consumer characteristics, as stated below: (uni0032uni0031)uni0009uni0009uni0009uni0009uni0009uni0009uni0009g2024(g1828uni002Cg1852uni003Buni03B8)=g2024(g1874uni003Buni03B8)uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where (uni0032uni0032)uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1874 =g2009g3398g2025g1828g3397g2019g1852g3397g2239 is the expanded index function assumed to be linear in bid prices, B, and consumer characteristics, Z; g2009, g2025uni002C and g2019 are elements of the vector of parameters uni03B8uni003Buni0009and g2239 is an error term. 89 To illustrate, the probability of a consumer agreeing to pay a price premium by accepting both bids (as in Equation (19)) is stated as follows: (uni0032uni0033)uni0009uni0009uni0009uni03C0g2935g2935g3435g1828g3036g3022uni002Cg1852g3036g3439=uni0009uni0031g3398g1833g3435g1828g3036g3022uni002Cg1852g3036uni003Buni03B8g3439uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 =uni0031g3398uni0009uni0031 (uni0031g3397euni0078g146g3435g3398(g2009g3398g2025g1828 g3036 g3022 g3397g2019g1852g3036 g3397g2239g3439))g3416 uni0009 In this study, the vector Z is comprised of consumer characteristics such as age, sex, household income, household size, level of formal education, awareness of aflatoxin contamination, region of residence, availability of peanut substitutes, peanut consumption frequency, among others. 3.5 Empirical Model and Information on Variables This subsection specifies the model to be estimated and presents a description of all variables used in this chapter. Due to the presence of inherently ordered WTP categories (i.e. uni03C0g2935g2935 g3408uni03C0g2935g2924 g3408uni03C0g2924), ordered logistic regressions are estimated using the LOGISTIC procedure in SAS (SAS Institute Inc., 2008). Specifically, the cumulative logistic regression is specified as follows: (uni0032uni0034)uni0009uni0009g142g145uni0067g139g150g4672wg150g146uni005Fg145uni0072g134euni0072g3037g4673uni0009 =g2009g3037 g3397g2025(uni006Duni0061uni0078uni005Fg146uni0072g139g133e)g3397g2019g2869(g139g144g133g145uni006Deuni005Funi0067g146)g3397g2019g2870(uni0068uni0068g149g139uni007Aeuni0032) g3397g2019g2871(uni0061uni0067euni005Funi0067uni0072g145g151g146)g3397g2019g2872(eg134g151uni005Fg133g142uni0061g149g149)g3397g2019g2873(g149g151g132g149g150g139g150g151g150e)g3397g2019g2874(g149euni0078) g3397g2019g2875(uni0072euni0067g139g145g144uni0031)g3397g2019g2876(uni0061wuni0061uni0072eg144eg149g149)g3397g2019g2877(g136uni0072eg147g151eg144g133g155)g3397g2271uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where g2009, g2025uni002C and g2019uni2032g1871 are parameters to be estimated; g2271 is the error term with a cumulative logistic distribution; subscript j represents unique functions and intercepts for each category of the response variable; and all variables in the model are defined in Table 3.2. 90 Table 3.2. Definition of Variables Variable Name Variable Label Categories and Codes1 Units hhincome Household income per month --------- Ghana cedis income_gp Income group of household (1=Low ) and 2=High --------- hhsize Household size --------- --------- hhsize2 Category of household size (1=Small) and 2=Large --------- max_price Maximum bids offered --------- Ghana cedis bid_index Bids relative to market prices --------- Percent premium Price premiums offered --------- Percent peanutp Existing peanut market prices --------- Ghana cedis amount Weekly peanut consumption --------- Cups age Age of respondent --------- Years age_group Age group of respondent 1=Young and (2=Old) --------- mstatus2 Marital Status of respondent (1=Married) and 2=Single --------- edu_class Education Level (1=Primary sch), 2=Middle sch, --------- 3=High sch, 4=College/University frequency Peanut consumption frequency (1=Low) and 2=High --------- wtp_order Willingness to Pay 1=No, 2=Yes-No, (3=Yes-Yes) --------- wtp_order2 Willingness to Pay 1=No and (2=Yes) --------- wtp Aflatoxin regulation 1=In favor, 2=Against, 3=Undecided --------- substitute Peanut substitutes 1=Yes and (2=No) --------- sex Sex of respondent (1=Male) and 2=Female --------- region1 Region of survey (1=Ashanti), 2=BA, 3=Central, --------- 4=Eastern, 5=Western aware_g General awareness of 1= Aware and (2=Not aware) --------- food contaminants awareness Aflatoxin awareness 1= Aware and (2=Not aware) --------- 1Reference levels of categorical variables used in regressions are in parentheses. Source: Survey Data Shortly, descriptive analyses on selected variables from the survey are provided; in Tables 3.3 and 3.4, we show summary statistics on relevant socioeconomic characteristics of consumers interviewed in the survey. Also, detailed discussions of some of the variables are introduced in the next section. Table 3.3 highlights the distribution of monthly household income in Ghana cedis (as at July 2012, the average exchange rate was 1 US Dollar = 1.8 Ghana cedis). 91 The median household income is 500 Ghana cedis per month for a typical household comprising of four individuals (see the distribution of household size in Table 3.3). Also, information in Table 3.3 suggests that the average household member who accepted to participate in the survey was 30 years old. Furthermore, the average household in Ghana consumes about 1.8 ?margarine cups? (i.e. 0.67kg or 1.5lbs) of shelled peanut every week.28 Table 3.3. Summary Statistics for the Continuous Variables Variable N Mean Median Std Dev Minimum Maximum max_price 652 2.8 2.5 1.1 1.2 7 bid_index 652 184.1 166.7 73 100 466.7 premium 652 84.1 66.7 73 0 366.7 amount 652 1.8 1.8 1.1 0.3 10 peanutp 652 1.5 1.5 0.2 0.9 2 hhincome 652 577.6 500 342.8 80 4000 age 652 32.9 30 9.6 18 68 hhsize 652 4.5 4 2.4 1 20 Note: Prices and income are stated in Ghana cedis (1 U.S. Dollar=1.8 Ghana cedis). Source: Survey Data Interestingly, from Table 3.4, nearly a half of the survey sample is made up of individuals who described their marital status as ?single?, whereas the remaining participants indicated their status as ?married?. The ?single? group of respondents consists of people who are widowed, separated or have never been married. The survey apparently captured many female household members than males, as displayed in Table 3.4. The imbalance in gender representation is partly explained by lower interview decline rates among females as opposed to males. 28 Six local ?margarine cups? equal one ?olonka cup?. On average, an ?olonka? of shelled peanut weighs 2.24 kg or 4.93 lbs. Hence, one ?margarine cup? of shelled peanut would approximately weigh 0.37 kg or 0.82 lbs. The present survey adopted ?margarine cup? as the standard measure. See Nagai (2008) for details on local units of measurement for some cereal grains and legumes in Ghana. 92 Also, in most cases where two or more members of a particular household were present, females were unanimously chosen by the other members to participate on behalf of the household. Hence, the female dominance in the survey is due to their key roles in household decisions concerning food; namely market transactions, food handling and storage, meals preparation, among others. Furthermore, the distribution of formal education levels shown in Table 3.4 indicates that three-quarters of the individuals interviewed have had at least 9 years of formal schooling at the ?Middle School? level (or junior high school level). 93 Table 3.4. Summary Statistics for the Discrete Variables Variables Categories Frequency Percent Cumulative Frequency Cumulative Percent age_group Young 423 64.88 423 64.88 Old 229 35.12 652 100 edu_class Primary sch 81 12.42 81 12.42 Middle sch 269 41.26 350 53.68 High sch 211 32.36 561 86.04 Coll/Univ. 91 13.96 652 100 substitute No 67 10.28 67 10.28 Yes 585 89.72 652 100 sex Female 406 62.27 406 62.27 Male 246 37.73 652 100 mstatus2 Married 341 52.3 341 52.3 Single 311 47.7 652 100 awareness Aware 44 6.75 44 6.75 Not Aware 608 93.25 652 100 frequency Once a week 186 28.53 186 28.53 2-3 times a week 240 36.81 426 65.34 4-6 times a week 93 14.26 519 79.6 Daily 88 13.5 607 93.1 Other 45 6.9 652 100 aware_g Aware 333 51.07 333 51.07 Not Aware 319 48.93 652 100 wtp In favor 512 78.53 512 78.53 Against 106 16.26 618 94.79 Undecided 34 5.21 652 100 Source: Survey Data 94 3.6 Results and Discussion This section provides results from the survey data in addition to observations made on the field through interviewers? interactions with survey participants. 3.6.1 Awareness of Aflatoxin Contamination The survey asked respondents if they had ever heard about the aflatoxin contamination problem in food products, particularly in peanuts. As revealed in Table 3.4, people?s familiarity with the aflatoxin issue is low in that approximately 9 out of every 10 individuals were not aware of the aflatoxin problem. This low level of aflatoxin awareness is consistent with Jolly et al. (2006) who found substantial evidence of little awareness among study participants in Ghana in 2002. However, the interviewers observed that most participants consider visibly moldy foods (including peanut) as unwholesome for human consumption. Hence, the apparent low level of aflatoxin awareness may, in fact, be less troubling since the average person in Ghana considers moldy foods as unhealthy for consumption. Moreover, the survey attempted to find out about consumers? knowledge of food contamination in general (see Table 3.4). Slightly more than half of the respondents demonstrated some awareness of issues regarding food contamination. The dominant concern expressed by the consumers was about chemical residues in food crops owing to the excessive use of synthetic pesticides and inorganic fertilizers in farming. 3.6.2 Willingness to Pay for Aflatoxin-free Peanut This subsection provides information on the key objective of the survey. Respondents were asked to cast their vote to reflect their willingness (or otherwise) to pay price 95 premiums for aflatoxin-free peanuts. The referendum was conducted after survey participants had received concise information on the aflatoxin contamination issue and implications of its regulation, namely the availability of aflatoxin-free peanut in local markets but at higher retail prices. By inspection, one can conclude that consumers in Ghana are willing to pay more for safer peanut (see Table 3.4). That is, approximately 79% of the survey participants voted in favor of aflatoxin regulation interventions that would ensure the availability of aflatoxin-free peanut, despite the attendant increase in prices. This result was obtained in the face of persistent reminders that consumers had the option to vote against the proposition (in favor of an alternative world where there would be different grades of peanut in retail markets, and the decision to buy sorted or unsorted peanut would rest entirely with the consumer). The participants who voted in favor of aflatoxin regulations expressed worries about the alternative scenario since, in their opinion, sellers of peanut paste (or butter), as well as food vendors (locally called ?chop bars?) are often suspected of using unwholesome peanut in processing food products. Over the survey period, the average market price of shelled and uncooked peanut was 1.5 Ghana cedis per cup (see Table 3.3). The survey shows that given the reference price of 1.5 Ghana cedis per cup, consumers are willing to pay 2.5 Ghana cedis per cup for aflatoxin-free peanut. This implies that consumers in Ghana are willing to pay a price premium of about 66% relative to existing peanut prices (see Table 3.3). However, mean WTP estimates obtained from the logistic regression are substantially less than the median value shown in the Descriptive Analysis (see the next subsection and Table 3.5). Specifically, the polytomous logit model indicates that respondents in the ?Yes-Yes? category are willing to pay a premium of 27% as 96 opposed to those in the ?Yes-No? category who would pay a 20% premium. Similarly, according to the binary logit regression results, participants who demonstrated some willingness to pay (i.e. the ?Yes? category) offered to pay 13% more than existing prices (see Table 3.5). It must be mentioned that some traditional markets in Ghana actually offer two grades of peanut for sale, where one is sorted thereby attracting higher prices compared to the unsorted counterpart. A few of the respondents also pointed out that superior-quality peanut (i.e. well sorted) are also sold in modernized grocery stores locally referred to as ?supermarkets?. 3.6.3 Factors Influencing Consumers? Willingness to Pay for Aflatoxin-free Peanut Table 3.5 shows estimation results obtained from cumulative (i.e. ordered) logistic regression models. Although the Score Test rejected the proportional odds assumption (i.e. equal slopes for all response-category functions but with different intercepts), we maintain the cumulative logistic regression results since the latent WTP-variable is ordinal. Furthermore, the Score Test is known to be non-conservative in that it has the tendency to reject the proportional odds assumption more frequently in favor of the alternative (Derr, 2013).29 Also, the alternative Generalized Logistic Regression yields potentially unstable results for the polytomous model due to small and empty cell entries revealed in preliminary analyses using bivariate contingency tables. The crosstabs were constructed for the response variable versus each of the categorical covariates (results from the contingency tables are not reported). Owing to the presence of low cell entries, the three-level WTP variable was collapsed to two 29Stokes, Davis, and Koch (2012) argue that small samples and/or cell frequencies often inflate the Score Test statistic. 97 categories. Therefore, binary ordered logistic regressions were also run with the response levels ?Yes-Yes? and ?Yes-No? combined as one category called ?Yes?. The purpose of the binary ordered logistic regression was to ascertain the robustness of results from the polytomous counterpart. The response variables in all models represent increasing levels of consumers? WTP for peanut with reduced aflatoxin contamination. Also, willingness-to-pay probabilities are cumulated over lower- ordered response values. In general, the concordance index indicates that the models perform well on the data in that observed probabilities for the outcome variable are correctly predicted in most cases. For ease of interpretation, discussion of results focuses on odds ratio estimates derived by taking exponents of the estimated regression parameters (see Appendix 3A for corresponding odds ratios computed from the cumulative logit models in Table 3.5). 98 Table 3.5. Model Estimation Results Polytomous Models Binary Models (Dep. Var=wtp_order) (Dep. Var=wtp_order2) Variables Category Model (1) Model (2) Model (3) Model (4) Intercept11 yes-yes -10.3126*** -10.9289*** ---- ---- (0.9184) (1.1032) ---- ---- Intercept12 yes-no -9.7769*** -10.3267*** ---- ---- (0.8933) (1.0772) ---- ---- Intercept2 yes ---- ---- -21.4009*** -30.1066*** ---- ---- (2.2996) (4.6309) max_price ---- 5.5525*** 5.6350*** 12.3507*** 16.4519*** (0.4963) (0.5157) (1.3513) (2.4741) income_gp high ---- 0.1690 ---- 1.1460*** ---- (0.1734) ---- (0.4275) hhsize2 large ---- -0.2038 ---- -1.0512** ---- (0.1768) ---- (0.4748) age_group young ---- 0.3424* ---- 1.2600*** ---- (0.1769) ---- (0.4408) edu_class coll/univ ---- -0.9022** ---- -1.8458* ---- (0.4466) ---- (1.0427) high sch ---- -0.0847 ---- -0.5880 ---- (0.2749) ---- (0.6462) middle sch ---- 0.5498** ---- 1.0745* ---- (0.2784) ---- (0.6122) substitute yes ---- 0.0416 ---- 0.1946 ---- (0.2602) ---- (0.5653) sex female ---- -0.1273 ---- -0.7193** ---- (0.1638) ---- (0.3603) region1 BA ---- -1.1730** ---- -0.0010 ---- (0.5438) ---- (1.6077) Central ---- 0.3355 ---- -0.4382 ---- (0.3858) ---- (0.9363) Eastern ---- -0.6988 ---- -1.2725 ---- (0.4281) ---- (0.9257) Western ---- 0.3382 ---- -0.1996 ---- (0.3614) ---- (0.8816) awareness aware ---- 0.3298 ---- -0.7889 ---- (0.3564) ---- (0.6620) frequency high ---- -0.1942 ---- -0.1984 ---- (0.1773) ---- (0.3844) Likelihood Ratio 474.344*** 508.426*** 571.723*** 609.042*** Concordance Index(c) 0.955 0.963 0.992 0.998 Number of observat?ns 652 652 652 652 Notes: 1. *** Significant at 1% ; ** Significant at 5%; and * Significant at 10% . 2. Standard errors are shown in parentheses. 3. Probabilities modeled are cumulated over lower-ordered response values. 99 Comparing the full polytomous and binary models ?? Models (2) and (4), respectively ?? we observe that the results are largely similar with expected signs even though household size and income appear important in Model (4), whereas region of residence matters in Model (2). Thus, results obtained from the polytomous logistic regression appear considerably stable. Nonetheless, the binary logistic model is preferred, especially in connection with the socioeconomic characteristics, given the relatively small sample size of the survey data. Hence, results from Model (4) are the main focus in this subsection although references are made to Model (1) in discussions regarding mean WTP estimates for the upper categories of the dependent variable. Applying Equation (11), the estimated median WTP derived from the simple polytomous model (i.e. Model (1)) are 1.9 and 1.8 Ghana cedis, respectively for the ?Yes-Yes? and ?Yes-No? categories of the outcome variable. The simple binary regression (i.e. Model (3)) also produces a median WTP value of 1.7 Ghana cedis for the ?Yes? group of respondents.30 These WTP estimates suggest that consumers are willing to pay median price premiums ranging from 20% to 27%, according to the polytomous model; whereas the binary logistic model shows the median WTP as 13% more than prevailing market prices. These price premiums are derived relative to the median market price of 1.5 Ghana cedis displayed in Table 3.3. On impact of socioeconomic characteristics, as far as consumers? WTP are concerned, Table 3.5 shows that household income, number of individuals in households, age of respondents, and gender are relevant factors that affect people?s 30 Notice that absolute values of the intercepts are used in computing the mean WTP estimates. Also, the price parameter (g2025) is positive, hence, a downward-sloping WTP curve can be derived after plugging the estimated slope into the index function shown in Equation (9) above. 100 behavior. In addition, the level of respondents? formal education appears to influence their WTP although the evidence is statistically weak. Conversely, the remaining consumer characteristics, namely the availability of peanut substitutes, region of residence, participants? awareness about aflatoxin contamination, as well as the frequency of peanut consumption have no impact at all on the variation in WTP. Specifically, compared to lower income households, participants belonging to the higher income group (i.e. with monthly household incomes exceeding 500 Ghana cedis) are about 10 times more willing to pay premiums, holding all other variables in the model constant. The preceding result is statistically significant at the 1% level. Furthermore, the odds of larger households paying price premiums are approximately 88% less than those of smaller households (i.e. with at most four individuals), for given levels of the remaining regressors. In other words, people from smaller households are more willing to pay higher prices for aflatoxin-free peanuts compared to participants from larger households, and this result is statistically significant at the 5% level. Also, at the 1% level of significance, the odds of younger respondents? WTP are 12 times greater than older survey participants. Precisely, respondents who were 35 years and below at the time of the survey were more likely to offer price premiums compared to older respondents. In addition, males are more likely to offer price premiums than females in the sense that the odds of female?s WTP is only 24% that of their male counterparts. Interestingly, individuals who have had at least college education are less likely to pay more for aflatoxin-free peanut compared to people who had a maximum of elementary schooling even though this result is only significant at the 10% level. Finally, socioeconomic characteristics such as participants? access to peanut substitutes, geographical location of the survey, whether individuals are familiar with 101 the aflatoxin problem or not, and the frequency at which households consume peanut do not influence WTP (even at the 10% level of statistical significance). 3.7 Summary and Conclusions The central goal of this chapter was to study consumer preference for peanut with reduced aflatoxin contamination, following concerns over negative effects of aflatoxin policy interventions on the economic welfare of food market participants. This topic shows empirical evidence of consumer willingness to pay (WTP) for safer peanut guaranteed through regulations; a subject that has been ignored in assessments of aflatoxin policy interventions. In addition, the study sought to determine some important socioeconomic factors that may affect people?s WTP for quality peanut. To achieve these objectives, a Contingent Valuation (CV) survey was conducted in the year 2012 on 652 individuals sampled from households in Ghana. The resulting survey data were analyzed using a semi-double-bounded dichotomous choice method based on the random utility theory. Consequently, cumulative (ordered) logistic regression models were estimated. Findings reveal that efforts at disseminating information on aflatoxins contamination must be intensified in order to improve on the existing level of awareness among people living in Ghana. Education campaigns to effectively raise awareness are required since most people consider conspicuously moldy foods as unhealthy but do not necessarily understand the adverse impacts of high levels of dietary aflatoxins exposure. Another revelation from the survey data is that a majority of consumers in Ghana are willing to pay price premiums for peanut with reduced aflatoxin 102 contamination. Specifically, the survey participants offered modest to high WTP values ranging from 13% to 66% greater than existing market prices. Furthermore, the study shows that socioeconomic characteristics such as income, family size, age, and gender actually influence consumers? willingness to pay premiums for quality/safer peanut. Particularly, improving income levels substantially affects people?s willingness to pay for aflatoxin-free peanut. Moreover, individuals associated with smaller households are more inclined to demand quality food by offering price premiums for safer peanut. The younger segment of the population in Ghana are relatively conscious about food safety, and are more willing to pay higher prices in order to consume peanut with reduced aflatoxin contamination. According to the 2010 Population Census, Ghana?s population is predominantly young with mean (median) age of 24 (20) years (Ghana Statistical Service, 2012). This means that the introduction of regulations would largely receive approval from the populace. Females in Ghana have been shown to be less willing to pay price premiums for aflatoxin-free peanut unlike the males. Given the integral role of women in the Ghanaian society ?? in terms of planning, purchasing, and preparation of meals at the commercial and household levels ?? it would be crucial for policymakers to target women in aflatoxin awareness campaigns and economic empowerment schemes. Another interesting finding was that consumer characteristics such as the availability of peanut substitutes, region of Ghana where the respondent lives, participants? awareness of aflatoxin contamination, the frequency at which households consume peanut, as well as formal education level have no effect on people?s WTP for safer peanut. To conclude, field observations together with analyses performed on the survey data show that consumers in Ghana are strongly in support of aflatoxin 103 regulation enforcement and are willing to pay price premiums for aflatoxin-free peanut. The findings provide encouraging signals to the research community and regulatory bodies regarding factual assessments of aflatoxin policy implications on food market participants ?? producers and consumers. Specifically, it has been shown empirically that the introduction of aflatoxin standards with the attendant price increases may not be harmful to consumers? economic welfare as it is widely believed. Knowing that consumers are willing to pay more for aflatoxin-free peanut also serves as incentive to stakeholders in the supply chain. Particularly, peanut producers would strive to comply with aflatoxin standards if there is substantial demand for food products with reduced aflatoxin content. 104 References Alfnes, F., C. Yue, and H. H. 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American Journal of Agricultural Economics 88(4): 947-960. 111 Appendix 1: Additional Information for Chapter 1 1A. Peanut Import Quantity (or Market) Shares for Exporters in the EU Market Exporters 1995-1998 1999-2002 2003-2007 1995-2007 China 0.2115 0.3115 0.267 0.2636 USA 0.2341 0.1918 0.1018 0.1702 ROW 0.5544 0.4967 0.6312 0.5662 Source: Computed from FAO Statistics (2010). Note: EU?s annual edible peanut import from each exporter is divided by total EU annual edible peanut imports and results averaged over the stated periods. 1B. Computation of Compliance Tax Rates and Price Transmission Elasticities In an attempt to model the EU regulation as a tax, the basic price equations are specified as below: g4666uni0034g4667g3398g4666uni0036g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1842g3036g3005 g3404uni0009g1842g3036g3020 g3397g1846g3036 g3397g1829g3036uni0009uni0009uni0009uni0009g1858g1867g1870uni0009g1861 g3404g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 where Ti is the per-unit transfer costs, and Ci is the per-unit compliance cost or ?tax.? Suppressing transfer costs, these equations are written in percentage changes as in equations (4?)-(6?) above, where uni0009g2009g3036 g3404 g3017g3284 g3253g2879g3021g3284g2879g3004g3284 g3017g3284g3253 are the price transmission elasticities, g2010g3036 g3404 g3004g3284g3017 g3284g3253 are the compliance tax rates, and R* is the uniform percentage increase in standards (compliance costs) caused by tighter regulation. All source-specific import prices were obtained from the FAOSTAT (2010) database as unit prices. Shipping cost for the USA, according to a Nicaraguan peanut sector study conducted by Oosterman (2000), is 87 US$/MT. Except for Argentina and some African countries (with shipping costs of 105 US$/MT and 200 US$/MT respectively), there are no available direct shipping costs for the other peanut exporters who make up the ROW. 112 Therefore, Jaffee?s (2003) research (cited by Hallam et al., 2004) which provides the cost of freighting green beans from different origins to the EU market was consulted. Consequently, China and ROW are assigned shipping costs of 250 US$/MT each. 1C. Estimating Export Supply Elasticities The export supply elasticities were computed from the equation: g4666uni0033uni0039g3398uni0034uni0031g4667uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g2013g3036 g3404g1857g3020 g3397g4666uni0031g3398g1863g1876g3036g4667uni007Cg1857g3005uni007Cg1863g1876 g3036 uni0009uni0009uni0009g1858g1867g1870uni0009uni0009g1861 =uni0009g1855g1860g1861g1866g1853uni002Cg1873g1871g1853uni0009g1853g1866g1856uni0009g1870g1867g1875 where eS and eD are, respectively, supply and demand elasticities for peanut in the domestic markets of the exporting countries and assumed to be identical across all exporters. kxi is the country-specific export share (i.e. share of total domestic production that is exported). Note that the demand elasticities substituted into the above formula are absolute values. Figures for exporters? domestic peanut supply and demand elasticities are from the elasticity database of the Food and Agricultural Policy Research Institute (FAPRI) cited by Beghin and Matthey (2003). Finally, values for export share of domestic production were computed from FAO Statistics. The table below provides details on parameters used to estimate the export supply elasticities shown in Table 1.2 above: 113 1C. Parameters Used to Estimate Export Supply Elasticities Parameter Definition Value eS Domestic own-price supply elasticitya 0.350 e D Domestic own-price demand elasticity a -0.200 kx1 Export share of China?s domestic peanut production 0.029 kx2 Export share of USA?s domestic peanut production 0.114 kx3 Export share of ROW?s domestic peanut production 0.050 Notes: aThe elasticities are assumed to be identical across all three exporters and all periods. 1D. Short-Run Effects with Export Supply Elasticities set to Zero (Perfectly Inelastic) Results shown in the tables below are obtained when peanut supply elasticities are perfectly inelastic. This simulation is carried out to highlight short-run impacts of EU standards tightening on peanut exporters. In addition, this exercise shows the scenario where export supply is less elastic than import demand in order to clearly demonstrate the demand and supply principle that the less elastic side of a market bears the greater incidence of a given policy. 1D1. Reduced-Form Elasticities for Peanut Prices and Quantities in the EU Market Variables No Substitution Effects Substitution Effects Included PcS* -0.1927 -0.1927 PusS* -0.8431 -0.8431 PrwS* -0.4174 -0.4174 P c D* 0 0 PusD* 0 0 PrwD* 0 0 Qc* 0 0 Qus* 0 0 Qrw* 0 0 Note: The effects on import demand prices are so small that they have been approximated to zeros. Thus, the actual results are -0.0000009, -0.0000008 and -0.000002 for demand prices paid to China, USA and ROW, respectively. 114 1D2. Exporter Welfare Changes (US$) Induced by 10% Regulation Costs Increase Exporters No Substitution Effects China -1487513 USA -4639729 ROW -5869964 1E. Exporter Welfare Changes (1,000 US$) Induced by 10% Tax Increase: No Substitution Effects Case Exporters 1995-2007a 1995-1998b 1999-2002c 2003-2007d China -74.85 -83.43 -24.09 -98.25 USA -750.37 -767.09 -795.88 -510.29 ROW -71.27 -64.8 -115.61 -53.29 Total -896.49 -915.32 -935.58 -661.83 aBaseline period. bFirst sub-period. cSecond sub-period. dThird sub-period. 1F. Price Transmission Elasticities, Compliance Tax Rates and Export Quantity Share Values for Sub-periods 1F1. Price Transmission Elasticities (alpha parameters) Exporters 1995-1998 1999-2002 2003-2007 China 0.5498 0.5899 0.5877 USA 0.6502 0.5041 0.3808 ROW 0.5021 0.2788 0.5618 1F2. Compliance Tax Rates (beta parameters) Exporters 1995-1998 1999-2002 2003-2007 China 0.1543 0.037 0.1235 USA 0.2606 0.4116 0.539 ROW 0.1835 0.498 0.114 115 1F3. Export Quantity Share Values Exporters 1995-1998 1999-2002 2003-2007 China 0.0269 0.0309 0.0287 USA 0.141 0.1207 0.0878 ROW 0.0506 0.0508 0.0491 1F. Vertical Shift in the Import Demand Curve Due to Regulations Using the model scenario where demand interrelationships are ignored, we compute the vertical (i.e. proportionate) shift in demand due to the regulation tax as follows: A vertical shift in import demand implies that the supply curve or quantity is fixed (i.e. vertical supply curve) at the initial equilibrium value (for more on vertical shifts in curves, see Muth, 1965; Kinnucan, Xiao, and Yu, 2000). Hence, there is no relative change at all in the quantity supplied i.e. g1850g3036uni2217uni0009=uni0030uni002E However, in equilibrium, uni0009g1850g3036uni2217 g3404uni0009g1839g3036uni2217 g3404uni0009g1843g3036uni2217 as shown in equations (10?)-(12?). This means that the demand relation in equations (16)-(18) can be rewritten as follows: uni0030g3404uni0009g2009g3036g2015g3036g3036g1842g3036g3020uni2217 g3397g2010g3036g2015g3036g3036g1844uni2217 Solving for g1842g3036g3020uni2217 yields the following price relation, representing the proportional demand shift on the price axis: g1842g3036g3020uni2217 g3404uni0009 g2010g3036g3398g2009 g3036 g1844uni2217uni0009g3407uni0030 Therefore, the vertical shift in demand is denoted as shown below: uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009g1848g3036g3005uni0009uni0009uni0009g3404uni0009 g3081g3284g2879g3080 g3284 g1844uni2217uni0009g3407uni0030 116 Appendix 2: Additional Information for Chapter 2 2A. Alternative forms of equations (19), (20), (21), and (22) in the text g4666uni0031uni0039uni2032g4667uni0009uni0009uni0009uni0009g1842g3020 uni2217 g1844uni2217 g3404uni0009 g2010 g2009uni0009g3428 g2013g3020 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667g3398uni0031g3432uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0032uni0030uni2032g4667uni0009uni0009uni0009uni0009uni0009g1843g3005 uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010g2015g3005 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0032uni0031uni2032g4667uni0009uni0009uni0009uni0009g1843g3025 uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010g2015g3025 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 g4666uni0032uni0032uni2032g4667uni0009uni0009uni0009uni0009g1843g3020 uni2217 g1844uni2217 g3404uni0009 g2013g3020g2010 g2009 uni0009g3428 g2013g3020 g2009g1837g3025g4666g2013g3025 g3398g2015g3025g4667g3398uni0031g3432uni0009g3407uni0030uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009uni0009 2B. Vertical Shift in the Tax-Burdened Domestic Supply Curve The vertical (i.e. proportionate) shift in domestic supply caused by regulation costs is computed as follows: A vertical shift in supply implies that the demand curve or quantity is held constant (i.e. vertical demand) at the initial equilibrium value (for more on vertical shifts, see Muth, 1965; Kinnucan, Xiao, and Yu, 2000). Thus, there is no relative change in the quantity demanded; g1843uni2217 g3404uni0030uni002E From Model One, equilibrium in the domestic market requires the following identity: uni0009g1843g3020uni2217 g3404uni0009uni0009g1843g3005uni2217 g3404uni0009g1843uni2217 Therefore, the tax-burdened domestic supply shown in equation (6) can be set to zero as follows: 117 uni0030g3404uni0009g2013g3020g2009 uni0009g1842g3005uni2217 g3398g2013g3020g2010g2009 g1844uni2217uni0009uni0009 Solving for uni0009g1842g3005uni2217 yields the following price relation, representing the proportional supply shift on the price axis: uni0009g1842g3005uni2217 g3404g2010g1844uni2217 Hence, vertical shift in the supply curve is denoted as follows: g1848g3020 g3404g2010g1844uni2217 uni0009 118 Appendix 3: Additional Information for Chapter 3 3A. Corresponding Odds Ratio Estimates from Model Parameters Table 3.5.1. Odds Ratio Estimates Polytomous Model Binary Model (Dep. Var=wtp_order) (Dep. Var=wtp_order2) Model (2) Model (4) Variables Category Estimate Lower CL Upper CL Estimate Lower CL Upper CL max_price ---- 280.063 101.924 769.545 >999.999 >999.999 >999.999 income_gp high 1.402 0.711 2.767 9.895 1.852 52.865 hhsize2 large 0.665 0.333 1.330 0.122 0.019 0.786 age_group young 1.983 0.991 3.968 12.429 2.208 69.957 edu_class coll/univ 0.262 0.063 1.084 0.041 0.002 1.070 high sch 0.593 0.209 1.685 0.143 0.013 1.557 middle sch 1.119 0.411 3.051 0.752 0.083 6.849 substitute yes 1.087 0.392 3.013 1.476 0.161 13.535 sex female 0.775 0.408 1.473 0.237 0.058 0.974 region1 BA 0.093 0.024 0.360 0.148 0.003 8.028 Central 0.422 0.166 1.072 0.095 0.010 0.921 Eastern 0.150 0.052 0.430 0.041 0.004 0.391 Western 0.423 0.176 1.016 0.121 0.015 0.959 awareness aware 1.934 0.478 7.821 0.206 0.015 2.765 frequency high 0.678 0.338 1.359 0.672 0.149 3.034 Likelihood Ratio 508.426 609.042 Concordance Index(c) 0.963 0.998 Number of observations 652 652 Notes: 1. "CL" means Confidence Limit i.e. 95% Wald Confidence Limits. 2. Probabilities modeled are cumulated over lower-ordered response values. 119 3B. Survey Questionnaire for Studying Consumers? Willingness to Pay for Aflatoxin-free Peanuts in Ghana Date of interview?????????????????????????????? ?. Name of interviewer?????????????????????????????... Region????????........?.. Capital???????.... Suburb????????. Interviewee Number /___/___/___/ Introduction Auburn University and KNUST, as members of USAID-sponsored Peanut CRSP team of investigators, are conducting this survey to assess Ghanaian consumers? demand/preferences for quality (or safe) peanuts in domestic markets. We will therefore be glad if you could grant us a few minutes of your time and objectively respond to questions we have for you. We assure you that opinions expressed will be strictly treated as confidential. A. Screening A1. Have you ever eaten peanuts and other peanut products before? 1.) Yes/___/ 2.) No/___/ (Terminate interview) A2. How often do you eat peanuts and peanut products? 1.) Once per week /___/ 2.) Two to three times per week /___/ 3.) Four to six times per week /___/ 4.) Daily /___/ 5.) Other/___/ (please specify)???????????????????????. A3. Based on the above codes (A2) please indicate the frequency at which you consume the following peanut products (Multiple Response Allowed). 1.) Soup, butter or paste /___/ 2.) Raw/uncooked kernels/___/ 3.) Boiled kernels/pods/___/ 4.) Roasted kernels/pods/___/ 5.) Peanut oil/___/ 6.) Peanut products like candies, cookies/pastry, fried peanut bars and kernels/___/ A4. What is your main source of peanut supply (Over 50%)? 120 1.) Own farm/producer/gifts/___/ (Terminate interview) 2.) Buys from market/___/ A5. About how many cups of shelled peanuts do you purchase weekly for your family needs? ???????????????????? cups per week. B. Awareness of Aflatoxin Contamination B1. Have you heard about any food contaminants that pose health problems to consumers? 1.) Yes/___/ 2.) No/___/ (Skip to B2) B1.1 If yes, please list????????????????????????????.. B2. Are you familiar with the problem of aflatoxin contamination in peanuts? 1.) Yes/___/ 2.) No/___/ (Skip to Section C) B2.1. (If ?yes? to question B2): How did you become aware of peanut aflatoxin contamination? For each of the sources below, please answer by indicating 1). Yes or 2). No: 1.) Through print/electronic media (e.g. TV, radio, newspapers) /___/ 2.) Through individuals like friends and other relations/___/ 3.) Through bodies like religious groups, NGOs/___/ 4.) Through workshops by universities and other government research institutions/___/ 5.) Others /___/ (please specify)???????????????????????? C. Market Description At this point, the interviewer MUST clearly and accurately explain the text below to all respondents before proceeding to Section D. This part is crucial since consumers must make informed decisions in the subsequent sections of the questionnaire. Market Description ?Given the warm and humid weather conditions in Ghana, peanuts are often contaminated with aflatoxins particularly during post-harvest handling and marketing. Aflatoxins are substances produced by molds (fungi) that cause people to fall sick when highly contaminated peanuts are consumed over time. Researchers have found associations between aflatoxins exposure and health problems such as aflatoxicosis, fever, jaundice, and liver cancer. Although environmental conditions make the elimination of aflatoxins nearly impossible, there are scientifically proven measures that could be adopted by peanut producers through retailers to minimize contamination. Activities that effectively reduce aflatoxins include proper drying, sorting, and hygienic practices. However, the procedures that achieve no/low 121 aflatoxin contamination involve additional costs (in terms of more labor and the discarding of contaminated peanuts) which could lead to higher retail prices. To protect the consuming public, government regulators in Ghana will have to enforce aflatoxin standards in the near future. In view of the above, we would want you to candidly answer the questions below by taking decisions in the context of your preferences, income, and regular food expenditure patterns.? D. Willingness to Pay, Demand and Preferences ?Please observe these three peanut samples ? A, B, and C ? for a moment. Note that sample A is unsorted and has the highest possibility of aflatoxin contamination. Sample B is fairly sorted (i.e. still contains broken and shriveled kernels) and has a lower possibility of aflatoxin contamination compared to A. Sample C has the least possibility of aflatoxin contamination since it is well-sorted and thoroughly cleaned. We would want you to answer a few questions shortly.? D1. If we asked you to make a choice, which of the samples will you first pick for consumption? 1.) Sample A i.e. unsorted peanuts/___/ 2.) Sample B i.e. moderately-sorted peanuts/___/ 3.) Sample C i.e. thoroughly-sorted peanuts/___/ D2. Based on the quantities of peanuts you buy per week for your family, if you were to buy the same number of cups indicate how many cups you would buy of each category (sample) at each of the prices below. Prices (GHC/cup) Peanut samples Less than 1.0 1.0 1.5 2.0 2.5 3.0 Above 3.0 GH Sample A i.e. unsorted peanuts Sample B i.e. moderately-sorted peanuts Sample C i.e. thoroughly-sorted peanuts D3. What is the approximate price of peanuts in your preferred market? (Please specify unit of measurement and whether shelled or unshelled)???????????????????... D4. In preparing your meals that typically include peanuts, do you have other substitutes/ingredients that you can use instead of peanuts? 1.) Yes/___/ 2.) No/___/ (Skip to D5) D4.1. If ?yes? to question D.4, please specify your peanut substitutes???????????. D4.2. What do you think can strongly influence you to switch away from peanuts to the substitutes you have listed? 1.) Prices/___/ 2.) Food safety reasons/___/ 3.) Others/___/ (please specify)????????????????????????. 122 D5. If the Government of Ghana organized a referendum calling on Ghanaians to express their opinions on a proposition to enforce peanut aflatoxin standards, what will your vote be? Please remember that the regulations will ensure the availability of aflatoxin-free (safer) peanuts in markets but could also mean that consumers will have to pay more than existing peanut prices. Please cast your vote. 1.) In favor/___/ 2.) Against/___/ (Skip to Section E) 3.) Undecided/___/ (Skip to Section E) D5.1. If vote is ?in favor? how much will you be willing to pay for a unit of aflatoxin- free peanuts???????????????????.................................................... . D5.2. Would you be willing to pay more if the true price of aflatoxin-free peanut turns out to be a little higher than you have stated above? 1.) Yes/___/ 2.) No/___/ (Skip to Section E) 3.) Not sure/___/ (Skip to Section E) D5.3. If ?yes? please specify the maximum price for aflatoxin-free peanuts beyond which you will no longer be willing to pay.??????.............................................................................. E. Attitudes and Behaviors Suggesting Food Safety Consciousness E1. Please rank the peanut forms below according to your intensity or frequency of consumption using alphabets A to F where A is the highest rank and F is the lowest rank. 1.) Peanut butter/soup /___/ 2.) Uncooked peanut kernels /___/ 3.) Boiled peanut kernels/pods /___/ 4.) Dry-fried or roasted kernels/pods /___/ 5.) Peanut oil /___/ 6.) Peanut products like candies, cookies/pastry, fried peanut bars and kernels /___/ E2. Which of the following best describes your habit regarding peanut purchases? 1.) Buys in bulk and use in bits over a period/___/ 2.) Buys in bits for one-time use only/___/ (Skip to E3) E2.1. If you buy in bulk, how do you typically store your peanuts? (Multiple Response Allowed) 1.) In a refrigerator/___/ 2.) Kitchen shelves/cupboard/___/ 123 3.) In a storage room with other food items/___/ 4.) Others/___/ (please specify)??????????????????????? E3. Which of the factors below do you normally give priority to before you decide to buy peanuts from a particular seller or group of sellers? (Multiple Response Allowed) 1.) Prices/Affordability/___/ 2.) Cleanliness/neatness of products/___/ 3.) Food safety concerns/health considerations/___/ 4.) Others/___/ (please specify)??????????????????????? E3.1. Out of the factors you have picked in E3, which one do you consider as the most important? 1.) Prices/Affordability/___/ 2.) Cleanliness/neatness of products/___/ 3.) Food safety concerns/health considerations/___/ 4.) Others/___/ (please specify)??????????????????????? F. Socioeconomic Characteristics F1. Gender 1.) Male/___/ 2.) Female/___/ F2. Marital Status 1.) Married/___/ 2.) Single/divorced/separated/widowed/___/ F3. Type of occupation 1.) Unemployed/___/ (Skip to F4) 2.) Self-employed/___/ 3.) Public servant or works for a private entity/___/ F3.1. Please specify your occupation????????????????????????. F4. Highest level of formal education 1.) No formal education or zero years of schooling/___/ 2.) Primary or 6 years of schooling/___/ 3.) JHS/Middle School or 9 years of schooling/___/ 4.) SHS or 12 years of schooling/___/ 5.) Training College/Polytechnic or 15 years of schooling/___/ 6.) University or 16+ years of schooling/___/ F5. Age of respondent????????????????years old. 124 F6. Number of people in your household??????????????????? F7. What is your household?s monthly income (including wages, salaries, remittances)????????????????????????????? ?. 1.) Below 300 Gh cedis/___/ 2.) From 300- 600 Gh cedis/___/ 3.) 601- 900 Gh cedis/___/ 4.) 900 and above Gh cedis F8. Have you had any health problems after you have eaten peanuts or peanut products? 1. Yes/___/ 2. No/___/ (Skip to F9) 3. Never noticed/___/ (Skip to F9) F8.1 If yes, please list?????????????????????????????.. F9. Has any other members of your immediate family had health problems after eating peanuts or peanut product? 1. Yes/___/ 2. No/___/ 3. Don?t know /___/ F9.1 If yes, please list???????????????????????????? 3C. Survey Questionnaire Guide for Enumerators Interviewers are required to pay particular attention to instructions provided below regarding specific sections in the questionnaire. A. Screening For question A1; if a respondent?s answer is ?NO,? politely terminate the interview and thank him for his time. For question A3; if a respondent indicates that he consumes ONLY peanut oil (i.e. option 5 of question A3) and no other forms of peanut then politely end the interview. For question A4; if a respondent obtains his peanut mainly (i.e. over 50%) from his own harvest and/or as gifts then politely terminate the interview. This study focuses on peanut consumers who buy from markets; ?out-of-pocket? consumers. C. Market Description Please endeavor to communicate the content of this section to respondents in very clear terms. The reliability of responses strongly hinges on how well the content of this particular section is conveyed. SECTION C Interviewers MUST clearly and accurately explain the text below to all 125 respondents. This part is crucial since consumers must take informed decisions in the subsequent sections of the questionnaire. Market Description ?Given the warm and humid weather conditions in Ghana, peanuts are often contaminated with aflatoxins particularly during post-harvest handling and marketing. Aflatoxins are substances produced by molds (fungi) that cause people to fall sick when highly contaminated peanuts are consumed over time. Researchers have found associations between aflatoxins exposure and health problems such as aflatoxicosis, fever, jaundice, and liver cancer. Although environmental conditions make the elimination of aflatoxins nearly impossible, there are scientifically proven measures that could be adopted by peanut producers through retailers to minimize contamination. Activities that effectively reduce aflatoxins include proper drying, sorting, and hygienic practices. However, the procedures that achieve no/low aflatoxin contamination involve additional costs (in terms of more labor and the discarding of contaminated peanuts) which could lead to higher retail prices. To protect the consuming public, government regulators in Ghana will have to enforce aflatoxin standards in the near future. In view of the above, we would want you to candidly answer the questions below by taking decisions in the context of your preferences, income, and regular food expenditure patterns.? D. Willingness to Pay, Demand and Preferences At this point, the interviewer must show all three peanut samples to the respondent for careful observation. Emphasize that although all peanut samples may be contaminated, the unsorted one (A) and the moderately sorted (B) are more likely to have higher aflatoxin levels than the sorted sample (C). Also stress that samples A and B are peanuts typically sold in Ghanaian markets. For question D3; find out about the price he normally pays for his peanuts in the appropriate quantity (i.e. whether per margarine can, ?olonka?, etc.). For question D5; again, remind the respondent to take decisions in the context of his preferences, income, and regular food expenditure patterns. F. Socioeconomic Characteristics For question F7; Actual household monthly incomes are preferred so please attempt to get precise income levels in addition to intervals. For question F7; Household should comprise of all individuals in a home that share meals and other basic necessities. Please Note: ?Multiple Response? at the end of a question indicates that respondents can choose one or more options.