mm 
 
 lANNINI • FOUNDATION 
 
 OF AGRICULTURAL 
 ECONOMICS 
 
 UNIVERSITY OF 
 CALIFORNIA 
 
 An Economic Evaluation of California 
 Avocado Industry Marketing Programs 
 
 1961-1993 
 
 Hoy F. Carman and R. Kim Craft 
 
 Si, "^IS9S 
 
 (^iannini Foundation Research Report Number 345^" ^^^^AF{y 
 
 July 1998 
 
 DIVISION OF AGRICULTURE AND NATURAL RESOURCES 
 CALIFORNIA AGRICULTURAL EXPERIMENT STATION 
 
AN ECONOMIC EVALUATION OF CALIFORNIA AVOCADO 
 INDUSTRY MARKETING PROGRAMS, 1961-1995 
 
 Hoy F. Carman and R. Kim Craft 
 
 Hoy F. Carman is Professor of Agricultural and Resource Economics, Uruversity of 
 California, Davis. R. Kim Craft is Assistant Professor of Managerial Economics, 
 Southern Utah University. 
 
 ACKNOWLEDGMENTS 
 
 A number of organizations and individuals supplied data and ideas that materially contributed to the completion 
 of this study. The financial support and data provided by the California Avocado Commission is gratefully 
 acknowledged. We also appreciate the cooperation of several firms who provided confidential data on their 
 advertising and promotion activities. Individuals who provided data and other important information include 
 Mark Affleck, Tom Bellamore, Val Weaver and Betty Bohrk of the California Avocado Commission, and Dale 
 Stem, Gwen Peterson and Gregg Payne. JuUan Alston and James Chalfant provided review comments on 
 earlier versions of the manuscript and Claudette Oriol prepared the final manuscript. Our sincere thaiJcs go to 
 all of them. 
 
SUMMARY 
 
 This report describes an economic study of the 
 California avocado industry, including its economic 
 history and markets, and presents an econometric 
 model of supply, demand, and price. The objective of 
 this study is to determine the effect of California avo- 
 cado industry advertising and promotion expendi- 
 tures on the demand and price for Califorrua avocados 
 and to estimate the ratio of benefits to costs for mar- 
 keting programs conducted by the California Avocado 
 Coiiunission.' The econometric model used to evalu- 
 ate the impact of industry advertising and promotion 
 includes components for avocado supply, demand and 
 equilibrium price. Following is a description of study 
 results for each of the major components. 
 
 Avocado Supply 
 
 The two major determinants of aimual avocado 
 production, average yields and bearing acreage, are 
 examined in some detail. Average yields, which are 
 responsible for sharp year-to-year variations in total 
 production, have become increasingly variable over 
 time. While yields demonstrated a rather steady up- 
 ward tiend from 1926 through 1956, there was little, 
 if any, trend evident after 1957. Possible explanations 
 for termination of the upward trend in yields and in- 
 creased variability include expansion of new acreage 
 on land not ideally suited to avocado production be- 
 cause of climate, soU quality or topography, and re- 
 duced water use due to sharp increases in water costs 
 in major production areas. 
 
 Avocado acreage changes armually as producers 
 make decisions on whether to plant new trees or re- 
 move existing trees. These decisions are hypothesized 
 to be based on expected profits over the bearing life 
 of new trees or the remaining life of existing trees. 
 Proxies for expectations based on recent prices, costs 
 and total returns, which have performed well in other 
 studies, were used to explain plantings, removals and 
 annual adjustments in both bearing acreage and total 
 acreage. Avocado acreage response equations found 
 (1) that plantings increase with increases in recent 
 average returns per acre adjusted for costs, (2) that 
 favorable income tax provisions for development of 
 groves led to increased plantings, and (3) that sharply 
 
 increased water costs were correlated with reduced 
 plantings ft-om 1990-91 through 1994-95. Removals 
 of avocado trees tended to respond most to the im- 
 mediate past year's costs and prices. The plantings 
 and removal relationships were combined in an esti- 
 mated equation for the annual change in bearing acre- 
 age which was used to represent annual acreage 
 response for California avocados in the simtdation 
 analysis described below. 
 
 The Demand for Avocados 
 
 California avocado prices and quantities trended 
 upward over the period considered (1962-95). How- 
 ever, in real terms, prices varied substantially aroimd 
 a slightly downward trend. At the same time, gross 
 producer revenues trended upward in both nominal 
 and real terms, indicating that growth in quantity 
 more than offset the decline in real prices. Overall, 
 there has been significant growth in the demand for 
 avocados over time. Factors associated with this 
 growth in demand are examined in some detail using 
 (1) an annual analysis of demand for the period 1962 
 through 1995, and (2) a monthly analysis of demand 
 for the nine marketing years 1986-87 through 1994-95 
 
 Annml Demand 
 
 An armual econometric model of the demand for 
 CaUfomia avocados, with annual average farm level 
 real price per poimd specified as the dependent vari- 
 able, was specified and estimated. The preferred 
 econometric model, which was selected on the basis 
 of statistical tests and economic theory, shows that the 
 quantity of avocados offered on the market is a very 
 important explanatory factor, having a strong, nega- 
 tive impact on price. The estimated price flexibility 
 of demand of -1.33 (at the average values for each of 
 the variables) means that a one-percent increase in 
 quantity supplied will cause a 1.33 percent decrease 
 in price, and a .33 percent reduction in gross revenue, 
 other factors constant. Demand is quite inelastic, as 
 indicated by year-to-year changes in production and 
 total crop revenues. Surprisingly, the quantity of 
 Florida avocados sold was found to have a positive 
 effect on California prices but, statistically, this effect 
 
 ' As used in this report, the tenns advertising or advertising and promotion include all marketing program activities de- 
 signed to increase the demand for California avocados. Expenditures (and the effects of such expenditures) for produc- 
 tion research, industry affairs, anti-theft programs and administration, which accoxmted for an annual average of 27.8 
 percent of GAG expenditures over the last five fiscal years (1991 - 1995), are not considered in the analysis. 
 
 iii 
 
was not significantly different from zero. Avocado 
 imports were foxmd to have a relatively large, and sta- 
 tistically significant, negative impact on California 
 avocado demand and prices. Real per capita dispos- 
 able income was foimd to have a large, and statisti- 
 cally significant, positive impact on avocado demand 
 and prices, confirming that avocados are a normal 
 good and that an increase in consiraier income leads 
 to a more-than-proportionate increase in demand. 
 
 The annual econometric model indicates that ad- 
 vertising and promotion had a positive impact on Cali- 
 fornia avocado demand and prices, and the point 
 estimate shows a price response of plausible magni- 
 tude (the estimated price flexibility is 0.13, indicating 
 that a one percent increase in advertising and promo- 
 tion expenditures leads to a 0.13 percent increase in 
 price, holding quantity constant). The estimated ef- 
 fect of advertising and promotion, which is not statis- 
 tically significant at the usual 95 percent level, is 
 significant at the 86 percent level. This lack of preci- 
 sion for the advertising variable may be the result of 
 data problems and other factors. These include mis- 
 matches between the CaHfomia and Florida crop years 
 that we were imable to correct (and probably resulted 
 in the unexpected positive relationship between 
 Florida sales and CaUf omia prices), the changing year- 
 to-year activities included in the advertising variable, 
 and possible structural changes. A monthly analysis 
 of demand for Califonua avocados was undertaken 
 as a partial solution to limitations evident in the an- 
 nual analysis. 
 
 Monthly Demand 
 
 The model of monthly demand for California avo- 
 cados was patterned after the annual demand model. 
 Average f.o.b. level monthly real price per pound was 
 specified as a function of pounds of avocados shipped 
 from California and Florida, imports, consumer in- 
 come, CAC marketing expenditures, brand advertis- 
 ing and promotion, prices of related goods, and 
 monthly demand shifters. Initial testing resulted in 
 deleting several variables from the analysis, includ- 
 ing the prices of possible related products and brand 
 advertising expenditures by California avocado pack- 
 ers. None was statistically sigruficant (t-ratios were 
 very small) in any of the formulations tested and it 
 was concluded that these variables have had no sta- 
 tistical effect on the monthly demand for all Califor- 
 nia avocados. The use of monthly data permitted close 
 matching of avocado sales from all sources, avoided 
 potential problems of structural change, and provided 
 the best available data on advertising and promotion 
 expenditures. 
 
 Results of estimating the monthly demand for all 
 California avocados were in line with expectations and 
 
 were a definite improvement over the annual model. 
 Each of the variables had the expected sign (Florida 
 sales had a negative impact on California prices), most 
 were statistically significant, and the magnitude of the 
 estimates was reasonable. Advertising and promo- 
 tion expenditures had a statistically significant posi- 
 tive effect on the price of (and demand for) California 
 avocados. The monthly and annual price flexibilities 
 of demand with respect to advertising and promotion 
 were almost identical (0.137 for the monthly analysis 
 vs. 0.130 for the annual analysis). Advertising and 
 promotion also had estimated lagged impacts on Cali- 
 fornia avocado prices and demand that extended five 
 months after the month the expenditures were paid. 
 The estimated price flexibility of demand of -1.54 is 
 larger than the annual estimate of -1.33, but the 
 monthly quantity variable includes both California 
 and Florida sales. The demand for California avoca- 
 dos at average prices and quantities is inelastic at both 
 the farm and f.o.b. levels, whether measured on an 
 annual or monthly basis. This means that total in- 
 dustry revenues wiU be less for a large crop than for a 
 small crop. 
 
 Estimated Benefit-Cost Ratios for Advertising 
 
 Measurement of benefits and costs for commodity 
 advertising are not as simple and straightforward as 
 they first appear. Depending on assumptions, there 
 are different measures of benefits, including average 
 and marginal benefits measured in the short nm (as- 
 suming fixed supply) or in the long nm (after adjust- 
 ment of acreage to price changes). For this study, fixed 
 supply benefits were estimated both aimuaUy and 
 monthly. The time horizon also affects the measure- 
 ment of costs. In the short run, aU costs of advertising 
 and promotion are paid by avocado producers. How- 
 ever, in the long run, producer adjustments to the as- 
 sessments used to fund advertising and promotion 
 act as a tax, which producers are able to partially shift 
 to buyers. Following are the range of benefit-cost ra- 
 tios estimated in the study. 
 
 The aimual fixed supply industry returns from 
 CAC advertising and promotion expenditures ranged 
 from a weighted average of $5.33 to $6.01 per dollar 
 spent depending on the time period examined and 
 the discount rate used (note that all returns are total 
 returns before the deduction of advertising expendi- 
 tures). A simple average of the annual fixed supply 
 benefit-cost ratios is equal to 5.25. Short term returns 
 for the most recent nine years (1986-87 through 1994- 
 95 marketing years), based on the monthly analysis 
 and discoimted at 3 percent, yields a weighted aver- 
 age return of $6.35 per doUar spent on advertising and 
 
 iv 
 
promotion. For the nine-year period of analysis, the 
 monthly marginal and average benefit-cost ratios are 
 equal to 8.92. The marginal benefit-cost ratios were 
 greater than one for all but two months of the period, 
 indicating that the CAC could have profitably in- 
 creased advertising and promotion during all but two 
 months of the nine-year period. 
 
 These returns are eroded over time, however, when 
 the acreage response to higher returns is factored into 
 the analysis. Producers make decisions in response 
 to higher returns that result in expanded acreage, but 
 there is a lag of several years before production in- 
 creases. Because demand is inelastic, increased pro- 
 duction decreases both price and total revenue and 
 production response may partially or totally offset 
 increased demand due to advertising. The armual 
 simulation model was run with actual and zero ad- 
 vertising and promotion expenditures and the annual 
 difference in total industry revenues was compared 
 to advertising and promotion expenditures. CAC 
 marketing program expenditures increased estimated 
 net total industry revenues by $102.8 million over the 
 period of analysis. In other words, estimated net in- 
 dustry total revenues after deduction of advertising 
 and promotion expenditures would have been $102.8 
 million lower than actually occurred, and the indus- 
 try would have been smaller, had the CAC not been 
 
 conducting its advertising and promotion programs. 
 When real costs and returns were discounted at 0 and 
 3 percent, the overall long-run discounted real returns 
 from advertising and promotion were $1.78 and $1.71 
 per dollar spent, if producers paid the total costs of 
 the program. After accounting for costs shifted to 
 buyers, we estimated that California avocado produc- 
 ers enjoyed an annual average benefit-cost ratio of 2.84 
 for the 34 years of the analysis. The long-run weighted 
 average benefit-cost ratios, when costs and returns are 
 discounted at 0 and 3 percent, are 2.48 and 2.26, re- 
 spectively. 
 
 On a month-to-month and year-to-year basis, the 
 industry has realized excellent returns from generic 
 advertising and promotion programs. Over time, 
 however, the supply response resulting from increased 
 returns can erode prices and net returns. As illus- 
 trated, avocados tend to exhibit cycles of production 
 and prices; attractive returns from advertising can 
 contribute to these cycles. This is the nature of the 
 short-run versus the long-run returns to advertising 
 when the industry does not control supply and there 
 is ease of entry and exit. Nevertheless, generic avo- 
 cado advertising and promotion has provided excel- 
 lent producer returns in both the short run and the 
 long run. 
 
 V 
 
TABLE OF CONTENTS 
 
 INTRODUCTION 1 
 
 THE CALIFORNIA AVOCADO INDUSTRY 2 
 
 Acreage Trends 2 
 
 Avocado Varieties 3 
 
 Location of Production 3 
 
 Stinactiare of Production 4 
 
 Asset Values 4 
 
 AVOCADO SUPPLY - 6 
 
 Average Avocado Yields 6 
 
 Acreage Response 8 
 
 Empirical Estimates of Acreage Response 11 
 
 Conclusions 15 
 
 THE DEMAND FOR CALIFORNIA. AVOCADOS 16 
 
 Characteristics of the Demand for California Avocados Over Tune 16 
 
 Other Factors Explaining the Demand for California Avocados 18 
 
 Advertising and Promotion 22 
 
 AN ECONOMETRIC MODEL OF ANNUAL AVOCADO DEMAND 25 
 
 Model Specification 25 
 
 Data Issues 26 
 
 Model Estimation and Testing 27 
 
 Further Tests of the Classical Box-Cox Regression Model 30 
 
 Conclusions 30 
 
 AN ECONOMETRIC MODEL OF MONTHLY AVOCADO DEMAND 32 
 
 Seasonal Sales and Prices 32 
 
 MONTHLY DEMAND MODEL SPECIFICATION 34 
 
 Data Series 34 
 
 The Advertising Variable 34 
 
 Model Estimation and Testing 35 
 
 Simultaneity of Supply and Demand 36 
 
 Statistical EHagnostics and Tests 36 
 
 Estimation Procedure 37 
 
 Estimation Results 37 
 
 ANALYSIS OF ADVERTISING BENEFITS AND COSTS 40 
 
 Analytical Model of Supply and Demand 40 
 
 AVOCADO SUPPLY RESPONSE 42 
 
 CONCLUDING COMMENTS 47 
 
 APPENDIX TABLES 48 
 
 REFERENCES 65 
 
 vii 
 
LIST OF FIGURES 
 
 Figure 1. California Avocado Acreage by Category, 1920-1995 2 
 
 Figure 2. CaHfomia Avocado Production by Variety, 1962-94 4 
 
 Figure 3. Average Annual Yields for California Avocados, 1925-95 6 
 
 Figure 4. Average Annual Avocado Yields by Variety, 1962-92 9 
 
 Figure 5. Annual Change in Bearing Acreage of California Avocados, Actual versus Predicted, 1953-95 14 
 
 Figure 6. Bearing Acreage of California Avocados, Actual versus Predicted, 1953-96 14 
 
 Figure 7. Average Annual Price of California Avocados in Nominal (A) and Real (B) Cents 
 
 per Pound (1995=100) 17 
 
 Figure 8. Annual CaUfomia Avocado Production and Average Real Price (1995=100) 18 
 
 Figure 9. Gross Annual Value of California Avocado Crop in Nominal (A) and Real (B) Dollars 19 
 
 Figure 10. U.S. Disposable Income per Capita in Real Dollars (1995=100) 20 
 
 Figvu-e 11. Total Annual U.S. Avocado Production by State, Plus Imports 20 
 
 Figure 12. California Avocado Shipments by US Regional Destination, 1994-95 Crop Year 21 
 
 Figure 13. CaUfomia Avocado Industry Annual Marketing Expenditures in Millions of Nominal (A) 
 
 and Real (B) Dollars (1995=100) 23 
 
 Figure 14. California Avocado Industry Armual Marketing Expenditures by Category, 1986-95 24 
 
 Figure 15. CaUfomia Avocado Industry Annual Advertising by Media Type, 1976-94 24 
 
 Figure 16. Hypothetical Market Response to Advertising 29 
 
 Figure 17. Seasonal Index of California Avocado Sales and Average Price, 1986-1995 33 
 
 Figure 18. Monthly Shifts in California Avocado Demand 39 
 
 Figxire 19. An Economic Model of Avocado Supply and Demand 41 
 
 Figure 20. Long-run Avocado Supply and Demand With and Without Assessments 41 
 
 Figure 21. A Recursive Simulation Model of CaUfomia Acreage, Production and Prices 43 
 
 Figure 22. CaUfomia Avocado Bearing Acreage, Actual and Simulated With and Without 
 
 Advertising, 1962-95 44 
 
 Figure 23. Estimated California Avocado Acreage Response to 10 Percent Price Increase: One 
 
 Shot in Year Zero and Permanent 44 
 
 Figure 24. Estimated Short-run and Long-run Benefit-Cost Ratios, 1961-62 through 1994-95 Crop Years 45 
 
 viii 
 
LIST OF TABLES 
 
 Table 1. Total California Avocado Acreage by Variety, 1950-1990 3 
 
 Table 2. California Avocados, Distribution of Farms by Acres Harvested, 1992 5 
 
 Table 3. Estimated Annual Average Yield Equations for California Avocados 7 
 
 Table 4. Estimated Annual Acreage Response Equations for California Avocados 12 
 
 Table 5. Definitions of Variables Used in Annual Demand Model 26 
 
 Table 6. Definitions of Variables Used in Monthly Demand Model 35 
 
 Table 7. Estimation Results for Monthly California Avocado (Inverse) Demand Model 38 
 
 ix 
 
LIST OF APPENDIX TABLES 
 
 Appendix Table 1. California Avocado Acreage by Category, 1920-1995 48 
 
 Appendix Table 2. California Avocado Acreage by County and Area, 1950-1990 49 
 
 Appendix Table 3. California Avocado Average Yields per Acre, 1925-1995 50 
 
 Appendix Table 4. New California Avocado Plantings Reported the Year of Planting and Up 
 
 to Eight Years Later, 1950-1992 51 
 
 Appendix Table 5. California Avocado Acreage by Category, Plantings and Removals, 1950-92 52 
 
 Appendix Table 6. Data Used in Annual Avocado Demand Model 53 
 
 Appendix Table 7. Monthly Sales and Average F.O.B. Prices for CaUfomia Avocados 55 
 
 Appendix Table 8. Monthly Shipments of Florida and Imported Avocados 56 
 
 Appendix Table 9. Macroeconomic Data Used in the Monthly Demand Analysis, CaUfomia 
 
 Avocado Crop Years 1985-88 57 
 
 Appendix Table 10. Bearing Acreage of California Avocados: Actual and Simulated With and 
 
 Without Advertising, 1961-62 through 1994-95 60 
 
 Appendix Table 11a. Estimated Annual Short-Rtm Benefit/Cost Ratios From Avocado 
 
 Advertising, 1961-62 to 1994-95 61 
 
 Appendix Table lib. Estimated Long-Rim Benefit/Cost Ratios From Avocado Advertising, 1961-62 to 
 
 1994-95 62 
 
 Appendix Table 11c. Estimated Long-Run Benefit/Cost Ratios From Avocado Advertising for 
 
 the Producers' Share of Costs, 1961-62 to 1994-95 63 
 
 Appendix Table lid. Projected Long-Rim Benefit/ Cost Ratios From Avocado Advertising, 
 
 Producers Pay All Costs and Producers Share Costs, 1995-96 to 2014-15 64 
 
INTRODUCTION 
 
 The objective of this study is to determine the 
 effect of Cahfomia avocado industry advertising 
 and promotion expenditures on the demand and 
 price for CaUfomia avocados and to estimate the 
 ratio of benefits to costs for marketing programs 
 conducted by the CaUfomia Avocado Commission. 
 These marketing activities, which were initiated 
 imder a CaUfomia state marketing order program 
 in 1961, continue to be ftmded by mandatory as- 
 sessments on aU Califomia avocado producers. The 
 report focuses on two questions: (1) the impact of 
 marketing expenditures on the demand and price 
 for CaUfomia avocados, and (2) whether net rev- 
 enues to producers resulting from the program 
 have increased enough to offset the program costs. 
 Answering these questions requires specification 
 and development of a detailed econometric model 
 of the Califomia avocado industry that includes 
 components for market demand and supply re- 
 sponse over time. 
 
 The organization of the report is based on the steps 
 taken to formulate answers to the research questions. 
 The initial step was to assemble a complete and reU- 
 able data base for the analysis. Using this data base, 
 we docimaent the changing pattems of avocado acre- 
 
 age, yields, production, and varieties that represent 
 the supply side of the industry, and then estimate a 
 model of industry supply response consisting of ex- 
 pressions for bearing acreage and average yields. The 
 analysis of supply is f oUowed by a description of the 
 demand for avocados that discusses prices and con- 
 sumption and presents time-series information on 
 important demand shifters, including income, popu- 
 lation, and advertising programs. An annual model 
 of avocado demand is then estimated and relevant 
 flexibilities and elasticities of demand are presented. 
 The annual demand model is supplemented with a 
 monthly analysis of demand based on the most re- 
 cent nine-year period. 
 
 The estimated supply and demand relationships 
 are used to simulate the economic benefits and costs 
 of the avocado industry advertising and promotion 
 program. The first step is to use estimated annual 
 avocado prices both with and without advertising to 
 derive net short-run returns to advertising and pro- 
 motion. Then, the acreage response relationship is 
 used to derive an estimate of long-run retums that 
 accounts for the impacts of producer supply response 
 over time to the price impacts of advertising and pro- 
 motion. 
 
 1 
 
THE CALIFORNIA AVOCADO INDUSTRY 
 
 Avocados are an important and high value fruit 
 crop with annual sales revenue ranking well within 
 the top ten CaUfomia fruit and nut crops. California 
 produces 85 to 95 percent of the annual U.S. avocado 
 crop, with Florida accounting for the remainder. The 
 demand for avocados has grown over time as a result 
 of growing consumer income, increasing population, 
 and industry-sponsored advertising programs, and 
 producers have responded by expanding planted acre- 
 age and production. Bearing acreage, for example, 
 remained under 25,000 acres imtil 1977 and total crop 
 value did not exceed $25 million vmtil the 1972-73 crop 
 year. Bearing acreage totaled 61,254 acres in 1994-95 
 (down from a peak of 76,307 acres in 1987-88), and, 
 for the most recent 5-year period (1990-91 through 
 1994-95 crop years), California's annual avocado pro- 
 duction and value averaged 345.5 million poimds and 
 $194.4 million, respectively. Aroimd this trend, avo- 
 cado production and prices vary substantially from 
 year to year as a result of variable yields and inelastic 
 producer-level demand. With inelastic demand, a 
 large crop returns less total revenue to producers than 
 does a small crop, other factors equal (the percentage 
 decrease in price is greater than the percentage in- 
 crease in quantity). Avocado producers tend to ex- 
 hibit extrapolative expectations behavior when 
 making crop investment decisions. They respond to 
 
 recent crop returns, expanding acreage and produc- 
 tion when returns have been favorable for several 
 years and decreasing acreage when recent returns 
 have been low. 
 
 Acreage Trends 
 
 Avocado production in California has a history 
 extending from 1856, when the first avocado tree im- 
 ported from Nicaragua was planted near Los Ange- 
 les. During the 1880s and 1890s, varieties were being 
 imported from Mexico and seedlings were being 
 grown. The beginning of a commercial industry is 
 placed at about 1910; by the 1919-20 crop year there 
 were 280 bearing and 235 non-bearing acres of avoca- 
 dos recorded in California (Appendix Table 1). Since 
 1920, the California avocado industry has experienced 
 three periods of expansion, with decreases in bearing 
 acreage following each expansion. As shown in Fig- 
 ure 1, bearing acreage of avocados increased steadily 
 from 280 acres in 1919-20 to 13,565 acres in 1946-47. 
 After a brief pause, bearing acreage again began to 
 grow through the 1950s, reaching 21,921 acres in 1964. 
 Increased new plantings from 1968 through the 1970s 
 fueled an expansion in bearing acreage from 20,715 
 acres in 1974-75 to a peak of 76,307 acres in 1987-88. 
 
Lower avocado prices as a result of increased produc- 
 tion in the 1980s, limited availability of suitable land, 
 increased urban pressures, high land costs, and high 
 water costs combined to reduce new plantings and 
 bearing acreage after 1987. The most recent estimate, 
 based on an aerial survey conducted during the 1994- 
 95 crop year by the Califomia Avocado Commission 
 (CAC), reports 1994-95 bearing acreage at 61,254 acres. 
 The CAC estimated 1995-96 bearing acreage at 59,577 
 acres after adjustments to the survey data for forecast 
 additions to bearing acreage and removals. 
 
 Avocado Varieties 
 
 More than 20 varieties of avocados have been pro- 
 duced commercially in Califomia since 1950. Of these, 
 four have had recorded acreage of more than 1,000 
 acres during any crop year and three others have had 
 more than 500 acres. The four varieties with over 1,000 
 acres include Bacon, Fuerte, Hass, and Zutano while 
 the three with 500 but less than 1,000 acres include 
 Pinkerton, Reed, and Rincon. The relative importance 
 of particular varieties has changed significantly over 
 time, as shown in Table 1. The Fuerte share of total 
 acreage decreased steadily from almost 79 percent in 
 1950 to just over 10 percent in 1990. It was largely 
 replaced by the Hass variety, which increased from 
 15.5 percent of total acreage in 1960 to over 71 percent 
 in 1990. In general, the Hass variety has two signiti- 
 cant advantages over other varieties; it typically has 
 the highest average yields and the highest average 
 prices per poimd. Other varieties, however, have dif- 
 ferent seasonal patterns of production and may be 
 better suited to particular locations. The Bacon 
 variety's share of total acreage increased from 2.5 per- 
 
 cent in 1960 to 11.3 percent in 1980 and then decreased 
 to 9.1 percent in 1990. Zutano acreage had a pattern 
 similar to that of Bacon, increasing from almost 3 per- 
 cent in 1960 to almost 9 percent in 1980 and then de- 
 creasing to 5.5 percent in 1990. 
 
 There was a change in the varietal composition of 
 production associated with the acreage changes de- 
 scribed above. Data on avocado production for three 
 variety categories (Fuerte, Hass and Other) illustrates 
 the shifts occurring. As shown in Figure 2, the Fuerte 
 variety often accounted for the majority of produc- 
 tion from 1962 through 1968, but then its share of to- 
 tal production decreased steadily to less than 5 percent 
 in 1994. The Hass variety's share of total production 
 expanded rapidly from just over 21 percent in 1962- 
 63, to over 83 percent in 1994-95, with the increase 
 coming at the expense of the Fuerte and Other vari- 
 ety categories. The Hass variety's increased share of 
 total production was due to its increased share of to- 
 tal acreage and its above-average yields. 
 
 Location of Production 
 
 Because of weather constraints, Califomia avocado 
 production tends to be concentrated near the coast in 
 Southem Califomia and in micro-climates that have 
 a low incidence of frost. Commercial production dis- 
 tricts used to describe the industry include the North 
 Counties (Santa Barbara, Ventura and San Luis 
 Obispo, the Mid<ounties (Los Angeles, Orange, Riv- 
 erside and San Bernardino), San Diego County, and 
 the San Joaquin Valley Coxanties (Fresno, Tulare and 
 Kem). As shown in Appendix Table 2, San Diego 
 County accoxmted for the majority of bearing acreage 
 from 1950 through 1975. As bearing acreage expanded 
 
 Table 1. Total Califomia Avocado Acreage by Variety, 1950-1990 
 
 Varieties 
 
 1950 
 
 1955 
 
 1960 
 
 1965 
 
 1970 
 
 1975 
 
 1980 
 
 1985 
 
 1990 
 
 
 
 
 
 
 Acres 
 
 
 
 
 
 Bacon 
 
 nr 
 
 nr 
 
 610 
 
 811 
 
 1,373 
 
 4,284 
 
 6,692 
 
 7,568 
 
 6,808 
 
 Fuerte 
 
 14,897 
 
 17,236 
 
 15,583 
 
 11,865 
 
 10,155 
 
 10,053 
 
 10,747 
 
 8,265 
 
 7,484 
 
 Hass 
 
 nr 
 
 nr 
 
 3,796 
 
 4,829 
 
 7,484 
 
 19,844 
 
 33,209 
 
 50,622 
 
 52,968 
 
 Pinkerton 
 
 nr 
 
 ru: 
 
 ru: 
 
 nr 
 
 ru: 
 
 nr 
 
 67 
 
 501 
 
 596 
 
 Reed 
 
 nr 
 
 nr 
 
 nr 
 
 ru- 
 
 nr 
 
 371 
 
 692 
 
 734 
 
 746 
 
 Rincon 
 
 ru' 
 
 nr 
 
 680 
 
 577 
 
 487 
 
 320 
 
 322 
 
 178 
 
 142 
 
 Zutano 
 
 nr 
 
 nr 
 
 725 
 
 576 
 
 857 
 
 2,833 
 
 5,291 
 
 5,536 
 
 4,145 
 
 Other 
 
 4,068 
 
 5,927 
 
 3,029 
 
 2,421 
 
 2,242 
 
 1,869 
 
 2,343 
 
 1,937 
 
 1,553 
 
 TOTAL 
 
 18,965 
 
 23,163 
 
 24,423 
 
 21,079 
 
 22,598 
 
 39,574 
 
 59,363 
 
 75,341 
 
 74,442 
 
 nr. acreage not reported separately. 
 
 Source: Califomia Agricultural Statistics Service, Califomia Fruit and Nut Acreage, annual issues. 
 
 3 
 
Figure 2. California Avocado Production by Variety, 1962-94 
 
 CS 
 
 CLi 
 
 100% 
 
 80% 
 
 60% - ■ 
 
 40% - 
 
 20% - 
 
 ■ Others 
 □ Hass 
 H Fuerte 
 
 1962 1966 1970 1974 1978 1982 1986 1990 1994 
 
 Year 
 
 rapidly after 1975, San Diego County's share of bear- 
 ing acreage dropped to 43 percent in 1980 and then 
 recovered to 49 percent in 1985 and 1990. The 1990 
 shares of acreage for the other production districts 
 were: North Counties, 34.5 percent; Mid-counties, 14.5 
 percent, and; San Joaquin Valley Counties, 2.0 percent. 
 The estimated 1995-96 shares of bearing acreage were: 
 San Diego County, 42.3 percent; North Counties, 41.0 
 percent; Mid-counties, 14.4 percent; and San Joaquin 
 Valley Counties, 1.7 percent (CAC). 
 
 Structure of Production 
 
 The California avocado industry is composed of a 
 large number of relatively small producers who ac- 
 count for a small proportion of total harvested acre- 
 age and a small number of large producers who 
 account for the majority of acreage. The 1992 Census 
 of Agriculture reported that 5,973 farms harvested 
 67,509 acres of avocados that year, resulting in aver- 
 age harvested acreage of 11.3 acres per farm. The 1987 
 Census coimted 5,920 farms with 79,270 acres of avo- 
 cados for an average harvested acreage of 13.39 acres. 
 As shown in Table 2, more than half (58.65 percent) of 
 the 1992 farms harvested fewer than 5 acres of avoca- 
 dos and these farms accotmted for orUy 10 percent of 
 total harvested acres. At the other end of the distri- 
 
 bution, 215 farms (3.6 percent) with more than 50 acres 
 of avocados accoimted for 42.8 percent of total acre- 
 age. The distribution of acreage between large and 
 small avocado producers is similar to many other 
 California tree crops. 
 
 Asset Values 
 
 The aggregate value of land and trees devoted to 
 California avocado production is relatively high, even 
 for a perennial fruit crop. Several factors are at work, 
 including the high value of the crop, high land val- 
 ues, and high development costs. The average per 
 acre revenues from avocado production are typically 
 high in comparison to many other crops; the land is 
 expeiTsive because climate requirements for avocado 
 production are ideal for many other crops, as well as 
 for people; and budgeted costs for establishment of a 
 new avocado planting have recently been estimated 
 at over $15,000 per acre (Livingston, et al., 1993). With 
 budgeted land costs of $15,000 to $16,500 per acre, total 
 costs to establish a new avocado grove were over 
 $30,000 per acre. A recent survey of Cahfomia land 
 values estimated 1996 San Diego Coimty avocado 
 grove values ranging from $5,000 to $15,000 per acre 
 (down from a range of $10-17,000 in 1993), Ventura 
 County avocado grove values ranging from $12,000 
 
 4 
 
Table 2. California Avocados, Distribution of Farms by Acres Harvested, 1992 
 
 Avocados Number of Total Acres Percent Percerit Average Cumulative Ciomulative 
 
 Acres Harvested 
 
 Farms 
 
 Harvested 
 
 of Farms 
 
 of Acres 
 
 Acres / Farm 
 
 % of Farms 
 
 /o oi /\crcS 
 
 .1 to .9 
 
 608 
 
 298 
 
 10.18 
 
 0.44 
 
 0.49 
 
 10.18 
 
 0.44 
 
 1 to 4.9 
 
 2,895 
 
 6,536 
 
 AO ATf 
 
 48.47 
 
 9.68 
 
 2.26 
 
 5o.o5 
 
 10.12 
 
 5 to 14.9 
 
 1,462 
 
 12,046 
 
 24.48 
 
 17.84 
 
 8.24 
 
 83.12 
 
 27.97 
 
 15 to 24.9 
 
 473 
 
 8,774 
 
 7.92 
 
 13.00 
 
 18.55 
 
 91.04 
 
 40.96 
 
 25 to 49.9 
 
 320 
 
 10,933 
 
 5.36 
 
 16.19 
 
 34.17 
 
 96.40 
 
 57.16 
 
 50 to 99.9 
 
 119 
 
 7,943 
 
 1.99 
 
 11.77 
 
 66.75 
 
 98.39 
 
 68.92 
 
 100 or more 
 
 96 
 
 20,979 
 
 1.61 
 
 31.08 
 
 218.53 
 
 100.00 
 
 100.00 
 
 Total 
 
 5,973 
 
 67,509 
 
 100.00 
 
 100.00 
 
 11.30 
 
 
 
 Source: U.S. Department of Commerce. 1992 Census of Agriculture. 
 
 to $25,000, and Santa Barbara County grove values 
 ranging from $13,000 to $35,000 per acre.' If one uses 
 a value of $20,000 per acre for groves in Ventura, Santa 
 Barbara, San Luis Obispo, Orange and Los Angeles 
 Cotmties and a value of $15,000 per acre for the re- 
 
 maining bearing acreage (San Diego County, liiver- 
 side Coimty, San Joaquin Valley and other), a conser- 
 vative estimate of the value of California's 1994-95 
 bearing acreage of avocados (61,254 acres) totals over 
 $1 billion. 
 
 ' These values are from California Chapter of the American Society of Farm Managers and Rural Appraisers, Trends in 
 Agricultural Land and Lease Values, Spring Ag Outlook Forum, March 20, 1996, pp. 14, 16. 
 
 5 
 
AVOCADO SUPPLY 
 
 The total supply of California avocados during a 
 given marketing season is the product of bearing acres 
 and average yield per acre. Most of the year-to-year 
 variability of total avocado production is due to the 
 variability of average yields. Bearing acreage tends 
 to change relatively slowly from year to year as a re- 
 sult of planting decisions made several years earlier 
 and current removal decisions. Average yields, on the 
 other hand, demonstrate large year-to-year variabil- 
 ity as a result of weather and other factors, including 
 alternate bearing tendencies of tree crops. In this sec- 
 tion of the report we discuss factors associated with 
 var5dng annual yields, and examine trends in aver- 
 age yields. We ttien examine factors associated with 
 bearing acreage adjustments over time, including the 
 effects of crop returns on plantings and removals. 
 
 Average Avocado Yields 
 
 Average annual yields for tree crops are influenced 
 by a nvimber of factors, including weather conditions 
 and altemate bearing tendencies. Over time, the age 
 distribution of trees, the introduction of new varieties 
 
 or cultural practices, and the availability of water will 
 also affect average yields. There is a lag of several 
 years from the time when a tree is planted imtil it be- 
 gins to produce commercial amoimts of fruit or nuts 
 (imder Califomia conditions the lag is three to five 
 years for avocados, depending on variety), 5delds in- 
 crease as trees mature, then remain high for an ex- 
 tended period of maturity, and finally decrease as the 
 trees become old or diseased. Recent budgets use a 
 projected tree life of 40 years. 
 
 A 70-year data series on average avocado yields 
 has been assembled from publications of the Califor- 
 nia Crop and Livestock Reporting Service and its suc- 
 cessor agency, the Califomia Agricultural Statistics 
 Service (Appendix Table 3). The pattern of average 
 yields for the 1925-26 through the 1994-95 crop years 
 is shown in Figure 3. Several interesting and impor- 
 tant observations on average yields are evident in 
 these data. First, while there has been a definite up- 
 ward trend in yields over the entire period, there is 
 no clear trend evident in the most recent half of the 
 period (1960 forward). Second, the year-to-year vari- 
 ability of average yields arovmd the trend appears to 
 
 Figure 3. Average Annual Yields for Califomia Avocados, 1925-95 
 
have increased substantially over time. This pattern 
 is consistent with yields varying by a constant per- 
 centage of an increasing average rather than by a con- 
 stant absolute amount. This percentage variation is 
 verified by plotting the natural logarithm of average 
 annual yields against time. The variability of the loga- 
 rithm of yields appears relatively constant over the 
 70 year period. We will specify and estimate alterna- 
 tive average jdeld equations to quantitatively describe 
 and partially explain yields over time. 
 
 In general terms, we expect average annual avo- 
 cado yields to be a function of alternate bearing, the 
 age distribution of trees, weather conditions, variet- 
 ies, and technology. Selection of the appropriate mea- 
 sures for these general variables is both important and 
 difficult. For example, there are several different ap- 
 proaches that could be used to model alternate bear- 
 ing. We foUow the lead of Alston, et al. (1995, p. 9-10) 
 and specify first-and second-order autoregressive 
 schemes to represent alternate bearing. Several other 
 variables are difficult to quantify accurately. Problems 
 arise with measuring the age distribution of tiees, since 
 available acreage statistics do not provide a corisis- 
 tent basis for deriving data series for mature and old 
 trees. Differences in yields by variety are best isolated 
 by estimating separate yield equations for each vari- 
 ety but availability of acreage and production data by 
 variety are restricted. While it is clear that weather 
 affects yields, it is difficult to assemble meaningful 
 annual data on rainfall, temperature and humidity that 
 help to explain the annual variation in yields. 
 
 Given the data available, a model including the 
 effects of alternate bearing and trends in average an- 
 nual yields over time was estimated for the period 
 1927 Ihrough 1995. The general form used for the yield 
 equation is: 
 
 Y.= «o + a,Y..,+a,(Y..,-Y,.,) + a3T, (1) 
 
 where, Y,is yield in poimds per bearing acre in year t, 
 Y, J and Y, ^ are yield per acre lagged one and two years, 
 respectively, and T^ is an annual time trend, with 
 1927=1, 1995=70. The estimated coefficients for the 
 average yield equation are shown in the first column 
 of Table 3, with t-statistics in parentheses below each 
 coefficient. 
 
 Results of estimating the average yield equation 
 over the entire period of 69 years are consistent with 
 the scatter diagram of yields over time shown in Fig- 
 ure 3. There is a positive and significant trend in yields 
 with average yields increasing 57.48 poxmds per year. 
 
 Table 3. Estimated Annual Average Yield Equations 
 for California Avocados 
 
 Explanatory Dependent Variable 
 
 Variable 
 
 \ 
 
 \ 
 
 
 
 estimated coefficients 
 
 Constant 
 
 
 997 15 
 
 4600 41 
 
 
 (2.71) 
 
 (2.9U) 
 
 {o.tyu) 
 
 Y 
 
 0.20 
 
 -0.57 
 
 0.04 
 
 
 (1.08) 
 
 (-1.66) 
 
 (0.15) 
 
 Y -Y 
 
 t-l t-2 
 
 -0.27 
 
 0.09 
 
 -0.18 
 
 
 (-2.14) 
 
 (0.38) 
 
 (-1.07) 
 
 T 
 
 57.48 
 
 158.76 
 
 19.96 
 
 
 (3.40) 
 
 (4.10) 
 
 (0.74) 
 
 
 0.53 
 
 0.63 
 
 0.08 
 
 Observation 
 
 1927-95 
 
 1927-56 
 
 1957-95 
 
 Period 
 
 Numbers in parentheses are t statistics. 
 
 Evidence of alternate bearing is limited, however, since 
 the estimated coefficient on one-year lagged yields is 
 not significantly different from zero and has an imex- 
 pected positive sign. Diagnostic tests performed on 
 the yield equation reveal the presence of 
 heteroskedasticity and structural change, problems 
 which must be corrected if one is to be confident of 
 estimated relationships. The Chow test indicates that 
 a statistically significant change in the structure of 
 yields occurred between 1956 and 1957. Thus, it is 
 not correct to assume that average yield coefficients 
 were equal over the entire period and separate equa- 
 tions must be estimated for the two periods 1927-1956 
 and 1957-1995.^ 
 
 The yield equation estimated for the first part of 
 the period (1927-1956, shown in the middle column 
 of Table 3) has a very sigiuficant trend in average yields 
 with an annual increase of 158.76 potmds. While the 
 coefficients on lagged jdelds have plausible signs, nei- 
 ther is significantly different from zero. Thus, there is 
 not strong evidence of alternate bearing during the 
 first portion of the period of analysis. 
 
 The analysis of factors associated with average 
 yields during the second portion of the period (1957- 
 95, shown in the third column of Table 3) is not at all 
 
 2 Estimation of the yield equations in logarithms will solve the problem of heteroskedasticity but the strucutural change 
 requires separate equations. Note that the yield equations estimated in logarithms, but without the difference in lagged 
 yields (Y, , - Y, ^ ) which were negative for some years, had resiilts very similar to those reported in Table 3. The results are 
 presented in levels for ease of interpretation. 
 
 7 
 
definitive, with the variables included explaining only 
 8 percent of the annual variation in average yields. 
 None of the estimated coefficients was significantly 
 different from zero. Thus, we conclude that there is 
 no recent trend in average avocado yields (either posi- 
 tive or negative), nor is there statistical evidence of 
 alternate bearing. 
 
 There is not an obvious explanation for the in- 
 creased variability of average avocado yields over time 
 or for termination of the upward trend observed for 
 many years. The average yields for most crops have, 
 in fact, continued to increase over time as improved 
 and new varieties have been introduced, as manage- 
 ment techruques have improved, and as trees reach 
 matvurity. One would have expected average avocado 
 yields to increase during the 1970s and 1980s as 
 sharply increased new acreage matured, and as the 
 mix of trees changed to the higher yielding Hass vari- 
 ety. Three factors could have contributed to termina- 
 tion of the upward trend and increased variability of 
 average yields. First, and most important, a signifi- 
 cant portion of the new acreage planted during the 
 1975-85 decade may have been less well-suited to avo- 
 cado production (or even marginal) than the core acre- 
 age because of climate, soil quality, or topography. 
 Second, the growth in average yields would be ex- 
 pected to level off as trees matured and the age distri- 
 bution of trees changed significantiy over time. While 
 a consistent series of acreage by age is not available 
 for the total period of analysis, ttie proportion of trees 
 over 11 years of age increased from about 31 percent 
 in 1982 to 89 percent in 1992. And third, sharp in- 
 creases in water costs in major production areas may 
 have resulted in reduced water use and this may be 
 associated with reduced yields in many groves.^ 
 
 Average yields by variety 
 
 Average avocados yields tend to vary by variety. 
 A set of data on bearing acreage and total production 
 for three variety categories (Fuerte, Hass and all oth- 
 ers) permits a limited comparison of average yields 
 by variety for the period from 1962 through 1992. Over 
 this period, the average annual yields were: Hass, 
 6,675 poimds per acre; Fuerte, 4,281 pounds per acre; 
 and all other varieties, 5,111 poimds per acre. There 
 were significant differences in shares of acreage and 
 total production because of differences in average 
 yields. In 1992, for example, the Hass variety ac- 
 counted for 72 percent of bearing acreage and 89 per- 
 cent of total production, while the Fuerte variety had 
 
 10 percent of bearing acreage and only 3 percent of 
 total production. AU other varieties had 18 percent of 
 bearing acreage and 8 percent of total production in 
 1992. 
 
 There are varietal differences in the year-to-year 
 variability of average yields. As shown in Figure 4, 
 Hass avocado acreage has experienced the highest 
 average yields, exceeding 10,000 pounds per acre four 
 times during the 1962-92 period, while Fuerte has had 
 the lowest yields, dropping below 2,000 povmds per 
 acre dviring five different crop years. The range be- 
 tween high and low yields dxiring the period was high- 
 est for the Fuerte variety at 8,806 pounds per acre (from 
 a minimum of 430 to a maximiim of 8,806 pounds per 
 acre). During the 1962-92 period, the coefficients of 
 variation (the standard deviation divided by the mean) 
 for annual yields by variety were: Fuerte, .5625; Hass, 
 .3476; and Others, .3283. It is not stirprising that analy- 
 sis of factors associated with annual changes in yields 
 by variety were similar to the analysis for all avoca- 
 dos for the 1957-95 period. That is, there is limited 
 evidence of any trend in average yields or of alternate 
 bearing. 
 
 Acreage Response 
 
 Actual and potential avocado producers make de- 
 cisions each year concerning whether or not to plant 
 new trees and, if the decision is positive, the number 
 of acres to plant. Existing producers also make deci- 
 sions on the removal of trees. These decisions, which 
 are based on the expected profitabiUty of avocado 
 production versus alternative investments, result in 
 net annual changes (either positive or negative) in 
 avocado acreage. If new plantings dxuing the year 
 exceed removals, total acreage expands; if new 
 plantings are exceeded by removals, total acreage de- 
 creases. Since avocado trees require 3 to 5 years after 
 planting to reach commercial production, positive net 
 increases in acreage will not have significant effects 
 on production for several years. These lags and the 
 implications for the formation of expectations that 
 affect the investment decision are very important in 
 modeling both acreage and production response. 
 
 The theoretical framework for models of perennial 
 crop producer supply response has been developed 
 and tested for several crops. Included are studies by 
 French and Matthews, Rae and Carman, Baritelle and 
 Price, BushneU and King, Thor and Jesse, French and 
 
 ^ One would not usually expect growers to limit the use of an input such as water on a high-value crop such as avocados. 
 Note, however, that water costs for some growers using municipal water supplies increased to over $400 per acre foot 
 and annual water applications have ranged to over four acre-feet per acre. Over the last decade, average total revenue 
 per acre has ranged from $1256 to $3757. 
 
 8 
 
Figure 4. Average Annual Avocado Yields by Variety, 1962-92 
 
 Bressler, Minami, French and King, Alston, Freebaim 
 and Quilkey, Alston, et al. (1995), and Carman and 
 Green, among others/ Data quality and availability 
 are important modeling constraints that often dictate 
 the nature of the supply relationships estimated for 
 perennial crops. Each of the avocado acreage data 
 series are examined in terms of strengths and possible 
 weaknesses. 
 
 The bearing acreage of avocados in year t is the 
 result of past plantings and removals decisions, as 
 noted above. This relationship can be expressed as: 
 
 BA. = BA.., + NP..,-R.., (2) 
 
 where BA is bearing acreage, the subscript t or time 
 designates the year, k is a lag of k years required from 
 the time when a tree is planted until it reaches bear- 
 ing age, NF is new plantings (acres) and R is acres 
 removed. Thus, explanation of the bearing acreage in 
 any year depends on explaining planting and removal 
 behavior lagged an appropriate number of years. 
 
 New Plantings 
 
 The acreage of new avocado trees planted during 
 any year is based on the expected profitability of avo- 
 cado production over the bearing life of the trees. Since 
 expectations cannot be observed, estimation of a 
 
 plantings equation requires specification of a set of 
 observable variables related to expectations. The stud- 
 ies referenced above typically employed a moving 
 average of recent past values of returns as a proxy for 
 expectations of future values of returns based on the 
 observation that producer expectations are often 
 formed from recent experience. Some studies used 
 gross returns, others used net returns. Some returns 
 were expressed in current dollars while others were 
 in real terms. 
 
 Income tax laws related to the tax treatment of 
 development experises, effective during a portion of 
 the study period, affected the after-tax costs of devel- 
 opment and are expected to have had an impact on 
 planting decisions. Income tax rules requiring capi- 
 talization of development expenses for citrus and al- 
 monds, effective in 1969 and 1970, resulted in a discrete 
 shift of investor interest from citrus and almonds to 
 other crops, including avocados, grapes and walnuts 
 (Carman). Then, tax law changes in 1976, that re- 
 stricted write-offs of orchard development costs for 
 Hmited partnerships, slowed syndication activity in 
 all orchard crops. A discrete change in tax laws, such 
 as occurred, is likely to change the parameters link- 
 ing production response to prices and other variables 
 in a one-shot fashion. This can be modeled by the use 
 
 * Alston, et al. (1995) include a summary of the main perennial crop supply response studies in the literature (pp. 16-22). 
 
 9 
 
of a dummy variable that has a value of one when the 
 law stimulates investment in new orchards (between 
 1971 and 1976) and zero otherwise. Such a dummy 
 variable can be used to aUow for a change in the inter- 
 cept, or in any of the model's parameters. 
 
 While one would expect other factors, such as the 
 expected profitability of alternative crops, to affect the 
 planting decision, attempts to include such factors in 
 estimated relationships have met with limited success. 
 
 Using previous work as a guide, the plantings 
 equation for avocados was specified as: 
 
 NP. = f(TRA,.,^,TAX,TA.) (3) 
 
 where NP, is acres of avocados planted in year t, TRA 
 is a lagged moving average of farm-level total rev- 
 enue per acre for avocados deflated by the index of 
 prices paid by farmers for all commodities, services, 
 interest, taxes and wage rates, (with the length of the 
 moving average, m, to be based on the data), TAX is a 
 zero-one variable to capture the impact of income tax 
 law changes and TA, is total avocado acreage in year 
 t. Given that profit expectations are based on recent 
 experience, new plantings are expected to have in- 
 creased as average returns increased. In addition, the 
 level of plantings required to maintain a given level 
 of acreage increases as acreage increases. 
 
 The California Agricultural Statistics Service pub- 
 lished annual estimates of the acreage of new avocado 
 trees planted through 1992. These data were based 
 on a combination of detailed acreage surveys from a 
 few counties each year and estimates provided by Ag- 
 ricultural Commissioners in the others. Close exami- 
 nation of these data reveal that plantings reported in 
 year t are typically much smaller than reported acres 
 of k-year old trees standing in a subsequent year t + k 
 (i.e., that were planted in year t). The delayed ap- 
 pearance in the data of plantings made in year t for a 
 nimiber of years is partially explained by the data col- 
 lection process. Counties would conduct a detailed 
 survey only once each 5 to 8 years and would esti- 
 mate acreage for the other years. It appears that the 
 estimates in intercensal years were based on known 
 new plantings, and there were always additional 
 plantings that were only discovered by detailed sur- 
 veys. This is illustrated in Appendix Table 4. The 
 first column of acreage figures are the new plantings 
 originally reported and estimated in year t. Reading 
 across a given row shows the acreage that was planted 
 in year t and reported standing from 1 to 8 years later. 
 For example, reading across the row for 1980, we see 
 that 3,636 acres of avocados were reported as planted 
 in 1980. In 1981, 4,556 acres of avocados were reported 
 as planted in 1980 and in 1982 the reported acres stand- 
 ing that were planted in 1980 increased to a maximvim 
 of 4,629. Acres reported as planted in 1980 remain in 
 a range of 4,026 to 4,198 acres thereafter. The bold- 
 
 faced entiles in the table are the maximum acres re- 
 ported as being planted in each year (t) and they typi- 
 cally occur several years after the planting date. 
 
 Removals 
 
 Avocado trees may be removed for various rea- 
 sons including low yields due to disease or age, be- 
 cause of persistent low returns, or to develop the land 
 for other uses. Lacking detailed data on age of trees, 
 incidence of disease, or the effect of urbanization, 
 empirical estimates of removal relationships for pe- 
 rennial crops have met with limited success. A note- 
 worthy exception was the cling peach study by 
 Minami, French and King, which had detailed acre- 
 age data by age category. Faced with data problems, 
 which were also present for avocados, most studies 
 have experienced difficulty in isolating variables that 
 are highly related to removals, and have used a con- 
 stant percentage of acres or a measure of profitability 
 at the time the removal decision was made. This study 
 specified the removals function as: 
 
 R. = f(TRA.-wBA.) (4) 
 
 where the variables are as defined above. One ex- 
 pects removals to increase when returns are low and 
 to decrease when retunns are favorable. Removals are 
 also expected to vary directiy with bearing acreage. 
 Since the data required to create variables to measure 
 other factors such as urbanization, disease, and tree 
 age were not available, their impacts wiU be included 
 in the cor\stant and error terms. 
 
 Removals of avocado trees are not typically re- 
 ported; construction of a removal series is based on 
 data that are reported. One can work from the total 
 acreage relationship to derive estimates of removals. 
 Begin by specifying year-to-year changes in total avo- 
 cado acreage (TA) as a fimction of plantings and re- 
 movals, as was done in equation (2) with bearing 
 acreage: 
 
 TA. = TA.., + NP..,-R,, (5) 
 Solving for removals, we have: 
 
 Rm = ta..,-ta, + np,, (6) 
 
 In terms of the acreage series reported, total acres 
 (TA,) in year t is the sum of bearing and nonhealing 
 acres in year t, and nonbearing acres are the siom of 
 new plantings for k years, where k is the nvmiber of 
 years after planting for avocados to be classified as 
 bearing. Since new plantings may not be counted for 
 several years, as noted above, and the data on 
 nonbearing acres are not usually updated, the direct 
 estimation of removals is subject to substantial error. 
 
 The estimation of removals is iUustiated in Appen- 
 dix Table 5, using the acreage figures reported in the 
 annual reports of the California Avocado Commission 
 (CAC). The bearing and nonbearing acres for each 
 
 10 
 
year are added to obtain total acres. The estimate of 
 removals is calculated according to equation (6), us- 
 ing maximum plantings reported in year t (Appendix 
 Table 4). A problem with the data series used to de- 
 rive removals is readily apparent; removals are nega- 
 tive in three years, 1968 (-629 acres), 1981 (-15,614 
 acres) and 1987 (-4,495 acres). Large negative remov- 
 als in 1981 are apparently due to discovery of previ- 
 ously uncounted acreage in the detailed acreage 
 surveys for San Diego, Santa Barbara and Ventura 
 Counties. The CAC conducted an aerial survey in 
 1987-88 that resulted in a sharp increase in reported 
 nonbearing acreage. 
 
 The problem of negative removals (and the dis- 
 covery of new plantings over time) is an indication of 
 other problems with the reported acreage series. Most 
 importantly, few of the series were revised as new in- 
 formation became available for previous years. CASS 
 did provide revised estimates for the previous year's 
 bearing and nonbearing acreage but these were not 
 incorporated in the past yield or production data. 
 Close examination of the revised estimates indicates 
 that most revisions were small and the revised series 
 did not solve the problem of negative removals. Thus, 
 one must allow for the newly discovered acreage in 
 each of the three years 1968, 1981 and 1987 when mak- 
 ing empirical estimates of acreage response. One can 
 work with the plantings series derived in Appendix 
 Table 4 to derive revised estimates of bearing and 
 noribearing acreage series but the accuracy of any re- 
 visions is unknovm.' 
 
 Empirical Estimates of Acreage Response 
 
 Several alternative approaches can be used to esti- 
 mate acreage response, with the choice usually based 
 on the availability and quality of data and liie pur- 
 pose of the estimates. Probably the most direct ap- 
 proach is to estimate the annual change in total 
 acreage, which is the difference between plantings and 
 removals or net investment in acreage of the crop. If 
 reliable data are available on annual plantings and re- 
 movals, a separate equation for each can be estimated 
 and these can be combined to calculate annual changes 
 in acreage. If the purpose of the acreage response 
 analysis is to estimate the armual production of the 
 crop, one can estimate a single equation for the net 
 change in bearing acreage that includes lagged val- 
 ues of the variables affecting planting and removals. 
 This equation is used to calculate bearing acreage, 
 which when mviltipUed by average yield, gives total 
 
 production. One can also estimate separate equations 
 for plantings and removals and use a runiung sum of 
 the most recent plantings to estimate nonbearing acre- 
 age, or a lagged value of planting to account for the 
 usual period required to reach bearing age. The alter- 
 native approaches used to estimate models of supply 
 response for avocados in this study include: (1) sepa- 
 rate equations for plantings and removals, (2) the an- 
 nual change in total acreage, or net investment, and 
 (3) the armual change in bearing acreage. 
 
 Planting and Removal Equations 
 
 The new plantings relatioriship specified above is 
 used as a basis for estimating an avocado plantings 
 equation using a linear specification. While the vari- 
 ables included in the equation explain a reasonable 
 percentage of the variation in armual plantings, the 
 Durbin-Watson statistic indicates that there is posi- 
 tive serial correlation. This could be due to use of an 
 incorrect functional form and/ or oirutted variables, 
 but is most likely due to measurement problems in 
 the plantings data with the most recent observations 
 being understated. The new plantings equation, esti- 
 mated from data for the period 1951-52 through 1992- 
 93, is shown in the first column of Table 4. NP^ acres 
 of avocados planted in year t, is the dependent vari- 
 able. The explanatory variables include TRA^ j^ (a 
 four-year average of total revenue per acre deflated 
 by the index of prices paid by farmers for aU produc- 
 tion items (1977=1.00) and lagged one year), DWater 
 (a dummy variable to capture the impact of sharply 
 increased water costs with a value of one from 1990- 
 91 through 1994-95 and zero for other years), DTax (a 
 dummy variable to capture the effect of income tax 
 incentives available from 1970-71 through 1975-76), 
 and the t-ratios are in parentheses. Note that the 4- 
 year average of deflated total revenue per acre lagged 
 one year, which was used as the proxy for profit ex- 
 pectations, yielded better statistical results than did 
 other averages of total revenue or prices. Each of the 
 estimated coefficients has the expected sign and, ex- 
 cept for the coefficient on the tax variable, all are sta- 
 tistically significant at the traditional 95 percent level. 
 The tax coefficient is not significantiy different from 
 zero. 
 
 While removals may be related to several factors, 
 oi\ly a proxy variable for profit expectations and bear- 
 ing acreage were included in the equation estimated. 
 There was also the problem of what to do with the 
 negative removal observations. Possible alternatives 
 were to: (1) change each negative value to zero. 
 
 ' Carman and Green used this approach but their simulations of bearing acreage were consistently above revised bearing 
 acreage during the 1970s. 
 
 11 
 
Table 4. Estimated Annual Acreage Response Equations for California Avocados 
 
 Explanatory 
 
 
 Dependent Variable 
 
 
 Variables 
 
 NP, 
 
 
 ATA, 
 
 ABA, 
 
 
 
 
 
 
 
 Constant 
 
 -4,696.21 
 
 -552.17 
 
 -4,545.31 
 
 -3,640.848 
 
 
 (-7.37) 
 
 (-0.67) 
 
 (-5.39) 
 
 (-5.30) 
 
 TRA,., 
 
 3.90 
 
 0.67 
 
 (1.54) 
 
 3.42 
 
 
 (10.59) 
 
 (1.54) 
 
 (6.07) 
 
 
 
 
 
 
 2.885 
 
 
 
 • 
 
 
 (5.95) 
 
 
 
 
 
 • -7.169 
 
 
 
 
 
 (-1.69) 
 
 PR„ 
 
 
 
 
 28.206 
 
 t-1 
 
 
 
 
 (2.65) 
 
 D81 
 
 
 -17,867.74 
 
 18,432.14 
 
 12,246.90 
 
 
 
 (-13.70) 
 
 (12.88) 
 
 (9.90) 
 
 D87 
 
 
 -7,045.33 
 
 6,257.44 
 
 
 
 
 (-5.27) 
 
 (4.37) 
 
 
 DWater 
 
 -2,674.06 
 
 
 -2,934.13 
 
 -2,544.724 
 
 
 (-4.96) 
 
 
 
 (-3.35) 
 
 DTax, 
 
 1,097.48 
 
 
 1,053.61 
 
 
 
 (1.84) 
 
 
 (1.17) 
 
 
 DTax,.5 
 
 
 
 
 1 753 65 
 
 
 
 
 
 (2.89) 
 
 TA. 
 
 0.024 
 
 
 
 
 
 (3.52) 
 
 
 
 
 BA 
 
 
 0.033 
 
 
 
 t 
 
 
 (3.79) 
 
 
 
 
 0.85 
 
 0.84 
 
 0.89 
 
 0.92 
 
 D.W. 
 
 0.69 
 
 1.99 
 
 1.87 
 
 2.12 
 
 Observation 
 
 1950-93 
 
 1950-95 
 
 1950-95 
 
 1952-95 
 
 Period 
 
 
 
 
 
 Numbers in parentheses are t statistics. 
 
 (2) revise the acreage series to remove the problem, or 
 
 (3) remove their effect w^ith a dummy variable for each 
 year they occurred. The dummy variable approach 
 was used. The estimated equation for removals is in 
 the second column of Table 4. The ntimber of acres 
 removed in year t (R,) is the dependent variable. In- 
 dependent variables in the removals equation in- 
 cluded the proxy variable for expected profits (a 
 four-year average of total revenue per acre deflated 
 by the index of prices paid by farmers for all produc- 
 tion items (1977=1.00) lagged one year (TRA,.^ ^), D81 
 and D87 (the dummy variables for negative removals 
 in 1981 and 1987), and bearing acreage (BA,). The co- 
 
 efficients on the dummy variables and bearing acre- 
 age each have the expected sign and are statisti- 
 cally significant at the 99 percent level. The 
 estimated coefficient for deflated total revenue per 
 acre has an unexpected positive sign but it is not 
 significantly different from zero (the t-statistics are 
 in parentheses) at the traditional 95 percent level. 
 The variables included in the equation explain 84 
 percent of the variation in removals and there is 
 no evidence of autocorrelation. Variables for in- 
 come taxes, high water costs, and negative remov- 
 als in 1968 were not significant and were not 
 included in the final equation. 
 
 12 
 
Change in Total Acres 
 
 The change in total avocado acreage from year t-1 
 to year t is the difference between plantings and re- 
 movals, or net investment. The estimated equation 
 for net investment (ATA^), in the third column of Table 
 4, includes the independent variables used to estimate 
 plantings and removals. Each of the coefficients has 
 the expected sign and all are significant except for the 
 dummy variable for tax law changes affecting devel- 
 opment costs. 
 
 Change in Bearing Acres 
 
 The bearing acreage data series is the most impor- 
 tant series in working with supply response and it is 
 also probably the most accurate of the acreage series 
 available. Rather than estimate bearing acreage di- 
 rectly from equation (2), lagged bearing acreage is sub- 
 tracted from both sides and the annual change in 
 bearing acreage (ABA, = BA,- BA,_j) is estimated. The 
 formulation is similar to the net investment equation 
 estimated above but with extensive lags on new 
 plantings because of the time required to reach bear- 
 ing age. The time to bring an avocado tree into bear- 
 ing varies from three to five years, but for the Bacon, 
 Hass and Zutano varieties that account for over 85 
 percent of acreage, three years is typical. Since it is 
 not unusual to have another year delay between the 
 planting decision and actual planting as a result of 
 land preparation and acquisition of nursery stock, the 
 usual lag between the decision to plant an avocado 
 tree and the time it reaches bearing age is probably at 
 least four years (k = 4). Lags of 4, 5 and 6 years for the 
 variables associated with plantings were examined; 
 the 5-year lag yielded the best statistical results. 
 
 The estimated change in bearing acreage equation 
 is shown in the fourth coltmm of Table 4. Each of the 
 coefficients has the expected sign and all except the 
 coefficient for the one-year lagged cost index were sta- 
 tistically sigriificant at the 95 percent level or greater. 
 The proxy variable for expected profits affecting plant- 
 ing (TRA,j^) is a four-year average of total revenue 
 per acre deflated by the index of prices paid by farm- 
 ers for all production items (1977=1.00) lagged five 
 years. The four year average yielded better statistical 
 results than did either a three or five year average. 
 The five year lag was also preferred to either four or 
 six years, based on standard statistical measures. The 
 proxy variables used for expected profits in the re- 
 movals equation were average prices and the cost in- 
 
 dex, each lagged one year.* It is not tmusual in em- 
 pirical work to find that removals respond to more 
 recent economic factors than do plantings, even 
 though one would expect profit expectations affect- 
 ing plantings and removals to have similar time hori- 
 zons. The 1987 dummy variable was not significant 
 and was deleted. Examination of the acreage data 
 shows that the adjustment that occurred in 1987 im- 
 pacted nonbearing and total acreage much more than 
 it did bearing acreage. Income tax laws had a signifi- 
 cant impact on plantings during the six year period 
 1970-71 through 1975-76; the DTax variable had a value 
 of one for each of those six years and zero otherwise. 
 The estimated coefficient on the DTax variable indi- 
 cates that the shift of investor interest (due to restric- 
 tions on citrus and almonds) increased total avocado 
 plantings by almost 1753 acres each year relative to 
 the "no tax law" period. The impact of taxes on change 
 in bearing acreage was lagged five years because of 
 the delays between planting and bearing noted above. 
 The estimated coefficient on the dummy variable for 
 the increase in water prices indicates that bearing acre- 
 age decreased almost 2545 acres each year during the 
 five years from 1990-91 through 1994-95. Further de- 
 creases of this magnitude are xmUkely; the industry 
 has worked closely with local water agencies to re- 
 ceive some relief, and the most vulnerable acreage has 
 already been removed. Unexplained variation in the 
 change in bearing acreage equation is relatively small. 
 
 The estimated change in bearing acreage equation 
 has estimated coefficients that are consistent with ex- 
 pectations, it explains a high proportion of the annual 
 variation in bearing acreage, and it is also the easiest 
 to work with in simulating acreage and supply re- 
 sponse to various policies and programs. As shown 
 in Figure 5, the equation does a reasonable job of pre- 
 dicting annual changes in bearing acreage, given val- 
 ues for each of the independent variables. When an 
 initial value forbearing acreage is entered, the change 
 in bearing acreage equation also provides estimates 
 that tiack total bearing acreage quite well.^ The com- 
 parison of actual and predicted bearing acreage in Fig- 
 ure 6 shows a tendency for the model to slightly 
 over-estimate bearing acreage during the early years 
 when acreage was comparatively stable and to vm- 
 der-estimate the total when acreage was expanding 
 rapidly. In overall terms, however, the estimated val- 
 ues are quite close to actual values during the 44 years 
 analyzed. 
 
 *■ Lagged price and the cost index were used after the four-year average of real total revenues lagged one year was not 
 
 statistically different from zero. 
 ^ The estimated change in bearing acreage for each year t is added to the estimated bearing acreage for the previous year, 
 
 t-1, to yield a cumulative summation of bearing acreage. 
 
 13 
 
Figure 5. Annual Change in Bearing Acreage of California Avocados, Actual versus Predicted, 1953-95 
 
 Figiire 6. Bearing Acreage of California Avocados, Actual versus Predicted, 1953-96 
 
 10000 
 
 0 I I I I — n — I — I — n — I I I — I — I I I I I I I I I I — n — i i i i — i — i i i — n — i — i — r— i — i i i i 
 
 1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993 
 
 Year 
 
 14 
 
Conclusions 
 
 Annual production of California avocados is the 
 product of bearing acreage and average yields. Aver- 
 age yields are largely determined by factors outside 
 the control of producers and vary significantly from 
 year-to-year. Bearing acreage, on the other hand, is 
 the direct result of producers' past decisions on 
 plantings and removals and it tends to trend up or 
 down over time. 
 
 Analysis of avocado yields over time permits sev- 
 eral observations. After trending up for many years, 
 average yields tended to level off and become more 
 variable. No tiends in yields are presently evident, 
 and we were not able to isolate significant alternate 
 bearing tendencies. Average yields tend to vary by 
 variety, with the Hass variety having the largest aver- 
 age yields. Tentative explanations for the recent ab- 
 sence of an upward trend and increased variability of 
 average avocado yields include recent sharp increases 
 in water costs in many production areas, an increas- 
 
 ing average age of bearing groves, and the possible 
 expansion of plantings on land that may not be ide- 
 ally-suited for avocado production because of climate, 
 soil quality, or topography. 
 
 The major components of avocado acreage re- 
 sponse, including plantings, removals, total acreage, 
 and bearing acreage were examined in some detail 
 with generally consistent results. An average of re- 
 cent total revenues per acre deflated by the producer 
 price index was an important determinant of new 
 plantings. The most statistically significant determi- 
 nant of removals was acres of bearing trees. The vari- 
 ables used to explain plantings and removals were 
 combined in an equation to estimate the annual 
 change in bearing acreage, with time lags to account 
 for the time required for a newly planted tree to reach 
 bearing age. These variables, combined with variables 
 to account for the impact of favorable income tax laws 
 and high water costs on plantings and removals, ex- 
 plained 92 percent of the variation for annual changes 
 in bearing acreage. 
 
 15 
 
THE DEMAND FOR CALIFORNIA AVOCADOS 
 
 Since the production of avocados during a mar- 
 keting year is determined by decisions and events 
 occurring in previous time periods, and because avo- 
 cados are largely corisumed fresh and not stored from 
 one crop year to the next, the quantity of avocados 
 placed on the market in a given year is essentially pre- 
 determined. Given these conditions, the annual de- 
 mand for avocados is best expressed in the price 
 dependent form (the inverse demand model), with the 
 annual price of avocados being explained by avail- 
 able quantities and other factors. Along with quan- 
 tity, the key factors explaining demand include prices 
 of related goods, the purchasing power of potential 
 customers, the size of the market in terms of number 
 of consumers, and consumer tastes and preferences. 
 This section presents a broad overview of historical 
 data that characterizes and explains the annual de- 
 mand for California avocados and then formalizes the 
 relationships with an econometric model. 
 
 Characteristics of the Demand for California 
 Avocados Over Time 
 
 Figure 7 graphs the average annual farm-gate price 
 of Calif orrua avocados in both nominal and real dol- 
 lars for the 1962-95 time period.* Panel A of the figure 
 shows that nominal avocado prices were quite vari- 
 able throughout most of the period but there was a 
 distinct upward trend in these prices (solid Unes in 
 the figures are linear time trends fit with ordinary least 
 squares). For instance, nominal prices increased from 
 an average of $0.16 per pound in the 1960s (1962-69) 
 to an average of $0.72 per poimd in the 1990s (1990- 
 95). Price variability also increased over the time span; 
 the standard deviation of prices was 0.06 in the 1962- 
 69 period and 0.32 for 1990-95. The picture changes 
 markedly when prices are adjusted for inflation (panel 
 B). In real terms, prices appear to have a roughly con- 
 stant mean and variance over time. Some broad ten- 
 dencies are evident, however, as inflation-adjusted 
 prices were relatively high and variable throughout 
 most of the 60s and 70s (averaging $0.92 per pound, 
 with a standard deviation of 0.36, for the 1962-80 pe- 
 riod), low and constant through the early and mid- 
 80s (averaging $0.40 per povmd, with a standard 
 
 deviation of 0.17, for the 1981-87 period), and then 
 generally higher and more variable again in the late 
 1980s and early 1990s (averaging $0.77 per pound, 
 with a standard deviation of 0.31, for the 1988-94 pe- 
 riod). The fitted linear time trend suggests that real 
 prices tended to decline somewhat over the entire 
 period.' 
 
 One of the most prominent factors explaining the 
 price of avocados for a given time period is the quan- 
 tity offered on the market. Figure 8 illustrates total 
 CaUfomia avocado production along with the aver- 
 age real price per pound for the 1962 through 1995 
 crop years. As shown in the figure, supply fluctuated 
 dramatically around an increasing trend for the pe- 
 riod, with aimual variability largely due to average 
 yields. Since 1981, however, there appears to have 
 been littie if any trend in production quantities and a 
 substantial increase in annual variation. The "Law of 
 Demand" holds that there is an inverse relationship 
 between price and quantity, when everything else is 
 held constant. Thus, an increase in supply to a free 
 market will have a negative effect on prices provided 
 aU other determinants are fixed. This phenomenon is 
 clearly seen in the avocado market, as real prices and 
 quantities moved in opposite directions in nearly ev- 
 ery year (the simple correlation coefficient for the two 
 series is -0.68). Thus, changes in production clearly 
 explain a great deal of the variation in avocado prices. 
 
 While avocado prices and quantities have an in- 
 verse relationship, gross annual revenues for Califor- 
 nia avocado producers trended upward at a 
 corisiderable rate over the 1962-95 span (Figure 9). For 
 example, in nominal terms the value of total farm sales 
 of avocados averaged about $14.0 million per year for 
 the 1962-69 period and increased over 14 fold to an 
 average of $201.6 million per year for 1990-95. The 
 rate of growth was less dramatic in real terms; never- 
 theless, inflation-adjusted annual crop values also 
 tiended upward strongly. The fitted linear time trend 
 shown in panel B of Figure 9 (soHd line) implies an 
 average growth rate in the real value of annual CaU- 
 fomia avocado crops of about 4.7 percent per year 
 throughout the period imder consideration. 
 
 A comparison of Figures 8 and 9 suggests an im- 
 portant point: producer-level avocado demand ap- 
 
 ' The nominal price was adjusted for inflation using the consumer price index (CPI) for all items with 1995 as the base, 
 obtained from The Economic Report of the President and recent issues of The Survey of Current Business. 
 
 ' The slope coefficient for the estimated trend line for the annual real price of California avocados was not statistically 
 different from zero; thus, the results are consistent with the hypothesis of no trend in real prices. 
 
 16 
 
Figure 7. Average Annual Price of California Avocados in Nominal (A) and Real (B) Cents per Pound 
 (1995=100) 
 
 A. Nominal 
 
 175 -J— 
 
 150 ■ ■ 
 
 T5 125 
 
 0 -I — I — I — 1 — I — I — I — I — I — r — I — r — I — I — I — I — I — I — I — I — I — I — I — ' — ' — ' — ' — ' ' ' ' ' ' 
 1962 1966 1970 1974 1978 1982 1986 1990 1994 
 
 Year 
 
 17 
 
Figure 8. Annual California Avocado Production and Average Real Price (1995=100) 
 
 pears to be inelastic. This means that, all other factors 
 equal, an increase in crop size wiU decrease both prices 
 and total crop revenue while a smaller crop will in- 
 crease prices and total crop revenue. Notice that since 
 1979, avocado quantity and real producer revenues 
 moved in distinctly opposite directioris in every year 
 but one (1989). Thus, increases in production tended 
 to be met with more-than-offsetting decreases in prices 
 and reductions in total producer revenues; similarly, 
 decreases in production tended to be met with more- 
 than-offsetting increases in prices and a rise in total 
 producer revenues. 
 
 Other Factors Explaining the Demand for 
 California Avocados 
 
 Abstracting from the irregxilar components of the 
 time series discussed above, tiie general picture of the 
 CaUforrua avocado industry that emerges between 
 1962 and 1995 is one of real prices tending to decrease 
 slightly, with quantities and total revenues increasing 
 over time. This overview indicates that the inelastic 
 producer-level demand for avocados has increased 
 (shifted outward) over the period. Here we describe 
 factors that can cause such shifts in avocado demand. 
 
 In principle the demand for any good, such as avo- 
 cados, is affected by the supply and demand relation- 
 ships for all related goods, both complements and 
 substitutes. For instance, complements tend to be 
 consumed together, so that an increase in the price of 
 one resvdts in a decrease in the demand for both (and 
 
 vice versa). Other goods may substitute well for each 
 other in consumption, so that an increase in the price 
 of one tends to induce consumers to switch to the other 
 relatively cheaper product (and vice versa). Thus, 
 demand analysis should account for the effects of pro- 
 duction and price changes in complement and substi- 
 tute goods. In the case of California avocados, 
 avocados grown elsewhere might be considered as 
 distinct yet closely related products. However, other 
 related goods — those likely to have a statistically sig- 
 nificant impact on avocado demand — ^have not been 
 identified at this point. 
 
 The purchasing power of consumers, as repre- 
 sented by real disposable income, is another impor- 
 tant explanatory factor in quantitative demand 
 analysis. Economic theory predicts that as the income 
 of market participants increases, avocado demand will 
 increase as well, assuming that all other factors are 
 held fixed and that avocados are a "normal" good. 
 Figure 10 shows a positive trend in U.S. real dispos- 
 able income per capita for the 1962-95 period. In ad- 
 dition, cyclical periods of expansion and recession in 
 the U.S. economy are reflected in per capita real in- 
 come — the annual growth rate for the time period has 
 a mean of 1.92 percent and a standard deviation of 
 2.17 percent. According to economic theory, the de- 
 mand for avocados should be affected by these 
 changes in consimier income. 
 
 The demand for California avocados may also be 
 affected by supplies from other production regions. 
 Figure 11 shows annual U.S. avocado production by 
 
 18 
 
Figure 9. Gross Annual Value of California Avocado Crop in Nominal (A) and Real (B) Dollars (1995=100) 
 
 A. Nominal 
 
 300 
 
 -50 -I — I — I — I — I — I — I — I — I — I — I — T—i — r— I — I — I — I — I — I — I — I — I — I — I — I — I — ' — " — ' ' ' ' ' 
 
 1962 1966 1970 1974 1978 1982 1986 1990 1994 
 
 Year 
 
 B. Real 
 
 300 
 
 0 -I — I — I — I — I— I — I — I — I — I — I — r — I — I — I — I — I — I — I — I — I — I — I — 1 — I — I — I — ' — ' — ' — ' — ' — ' — ' 
 
 1962 1966 1970 1974 1978 1982 1986 1990 1994 
 
 Year 
 
 19 
 
Figure 10. U.S. Disposable Income per Capita in Real Dollars (1995=100) 
 
 25000 
 
 20000 
 
 ^ 10000 
 
 5000 
 
 I I I I I I I I I 
 
 1962 1966 1970 
 
 I I I I I I I I I I I I " ' I ' I I I I ' I ~ 
 
 1974 
 
 1978 1982 
 Year 
 
 1986 1990 1994 
 
 Figure 11. Total Annual U.S. Avocado Production by State, Plus Imports 
 
 20 
 
Figure 12. California Avocado Shipments by US Regional Destination, 1994-95 Crop Year 
 
 Southeast 
 
 Other 
 
 Northeast 
 9% 
 
 East Central 
 
 8% 
 
 West Central 
 5% 
 
 California 
 39% 
 
 Southwest 
 22% 
 
 Pacific excluding CA 
 10% 
 
 Notes- Regions are defined by the following city /area destinations: Pacific Region-Hawaii, Los Angeles, Phoenix /Tucson, 
 Portland, Sacramento, San Diego, Seattle, San Francisco, Salt Lake City; Southwest Region-Albuquerque, San Antonio 
 DaUas/Ft Worth, El Paso, Houston, McCalUn, Oklahoma City; West Central Region-Denver, Kansas City, Minneapohs/ 
 St Paul St Louis; East Central Region-Chicago, Cincinnati, Cleveland, Detroit, Grand Rapids, Indianapolis Milwaukee; 
 Northeast Region-Baltimore /Washington, Boston, Buffalo, Hartford/New Haven, Philadelphia, Pittsburgh Richmond 
 New York- Southeast Region-Atlanta, Birmingham, Memphis, Miami, New Orleans, Orlando. Other mcludes other US 
 destinations (0.7%), Canadian exports (0.1%), and overseas exports (1.6%). Source: 1994-95 AMRIC Summary Reports, 
 California Avocado Commission. 
 
 location plus imports. As indicated, California typi- 
 cally accotints for 85 to 90 percent of total U.S. pro- 
 duction, with Florida accounting for the remainder." 
 Historically, the quantity of avocados imported to the 
 United States has been quite small. For instance, from 
 1962 through 1989 imports amounted to just over one 
 percent of annual U.S. production. Recent years, how- 
 ever, have seen substantial increases in foreign avo- 
 cado purchases, with imports averaging over 10 
 percent of armual U.S. production since 1990. While 
 Calif onua clearly dominates the U.S. market, avocado 
 supply from Florida and foreign producers is signifi- 
 cant and is expected to have a measurable (negative) 
 impact on California prices. 
 
 All else being equal, total demand shifts propor- 
 tionately with increases in the size of the market. In 
 this study, the "market" for California avocados is 
 assumed to be the entire U.S. economy; hence, the U.S. 
 population is the explanatory factor representing the 
 size of the market. It is no surprise to note that U.S. 
 population increased steadily over all years imder con- 
 sideration in this report. Specifically, U.S. population 
 increased from approximately 186.5 million persoi\s 
 
 in 1962 to approximately 263.1 million persons in 1995, 
 with an average annual growth rate of 1.05 percent 
 (the standard deviation of the annual growth rate for 
 the period is 0.14). The steady expansion of popula- 
 tion has, thus, exerted constant upward pressure on 
 total demand which must be accounted for in model- 
 ing avocado markets. 
 
 Regarding the extent of the market, Figvire 12 il- 
 lustrates the percentage of Califorrua avocado ship- 
 ments by U.S. regional destination for the 1994-95 crop 
 year. As shown in the figure, nearly 40 percent of all 
 California avocado shipments were intra-state that 
 year. The next largest shipment destination was the 
 Southwest, followed by the Pacific (excluding Cali- 
 f onua). Northeast, East Central, West Central, South- 
 east, and Other. Note that the "other" category 
 includes foreign exports amounting to about 1.7 per- 
 cent of the total. This shipment distribution is as- 
 sumed to be generally representative of historical 
 patterns. Thus, while sales are heavUy concentrated 
 in the West and Southwest, the market for California 
 avocados can be corisidered national in scope, with 
 smaU amounts of foreign exports. 
 
 The state of Hawaii also has a small avocado industry that on average produces about 025% of the total U.S. crop. 
 
 21 
 
Advertising and Promotion 
 
 Advertising is the final factor posited to explain 
 consumer demand for avocados. Industry advertis- 
 ing efforts are expected to increase demand through 
 providing information and changing preference pat- 
 terns. A major objective of this study is to determine 
 the extent to which generic advertising programs con- 
 ducted by the California Avocado Commission have 
 been successful in shifting the demand for California 
 avocados. 
 
 The California avocado industry conducted ge- 
 neric advertising programs imder a state marketing 
 order from 1961 through 1977 and has operated im- 
 der the California Avocado Commission law since 
 1978. The current Avocado Commission law allows a 
 maximum producer assessment of 6.5 percent of the 
 gross dollar value of the year's sale of producer avo- 
 cados to support all Commission activities, including 
 production research, industry affairs, marketing pro- 
 grams, and administration. It also provides that ex- 
 penditures for administrative purposes within the 
 maximum assessment shall not exceed 2.5 percent of 
 the gross dollar value of sales. Actual grower assess- 
 ments during the 1990s have been substantially be- 
 low the allowable maximum, ranging from a low of 
 3.0 percent in 1990-91 to a high of 5.25 percent in 1992- 
 93. The average annual assessment for the five crop 
 years 1990-91 through 1994-95 was 4.05 percent. A 
 key point that we will return to later is that the legis- 
 lation does not contain provisioixs for controlling the 
 supply of avocados placed on the market. 
 
 A review of annual reports of the marketing order 
 and commission programs indicates that the indus- 
 try spent over $116 million on advertising, promotion, 
 and related services ("marketing expenditures") from 
 initiation of the program in 1961 through the 1994-95 
 marketing year. Figure 13 gives total annual market- 
 ing expenditures in nominal and real dollars. Since 
 available funds are based on producer revenues, mar- 
 keting expenditures followed gross crop values fairly 
 
 closely over the period (compare Figure 13 with Fig- 
 ure 9). In fact, the correlation coefficient between mar- 
 keting expenditures and the prior year's crop value is 
 0.83 in terms of levels and 0.50 in terms of annual rates 
 of change, verifying the expected positive relation- 
 ship." Thus, program outlays trended upward 
 throughout the period in both nominal and real terms. 
 The overall rate of increase was, however, less than 
 that of gross crop value. For instance, the real trend 
 line in Figure 13, panel B, increases at an average an- 
 nual rate of 3.7 percent, one percentage point below 
 the trend Une for real producer revenue. In addition, 
 it should be noted that there appears to have been little 
 trend in real marketing expenditures since 1977.'^ 
 
 Specific marketing efforts conducted on behalf of 
 Cahfomia avocado producers have taken a variety of 
 forms. For instance, in their annual reports the Cali- 
 fornia Avocado Commission breaks total marketing 
 expendittires into the following seven categories: con- 
 sumer advertising, consumer promotion, trade adver- 
 tising, foodservice, public relations, international 
 promotion, and processed products. Figure 14 shows 
 the relative allocation of marketing funds by four 
 major categories (consumer advertising, consvimer 
 promotion, trade advertising, and all other) from 1986 
 through 1995. Overall, consumer advertising received 
 the greatest percentage of funds (averaging 41.4 per- 
 cent for the period), followed by trade advertising (25.1 
 percent), other (24.2 percent), and consumer promotion 
 (9.3 percent). Year-to-year allocations varied somewhat 
 but for the most part remained fairly constant, espe- 
 daUy if consumer advertising and promotion are con- 
 sidered together. Figure 15 illustrates the relative 
 allocation of California avocado advertising dollars by 
 media type from 1976 through 1994 (1995 data were 
 unavailable). The chart dearly shows magazine adver- 
 tising dominating other media types by a substantial 
 amotmt, receiving on average 78 percent of all advertis- 
 ing dollars for the given period. Moreover, magazines 
 were the only advertising media used prior to 1976. The 
 chart suggests that program administrators experi- 
 
 " If the common trend is accounted for, there does not appear to be a strong relationship between marketing expenditures 
 and the current crop value. For instance, the correlation coefficient is 0.60 in levels and -0.32 in armual rates of change. 
 This is an important point for the econometric estimation that follows. 
 
 " Note that proportion of CAC expenditures on marketing programs and administration has decreased in recent years as 
 expenditures on industry programs (production research, industry affairs and anti-theft programs) increased. Compar- 
 ing the average distribution of expenditures for two five-year periods, 1985-86 through 1989-90 with 1990-91 through 
 1994-95, shows that marketing program's share decreased from an annual average of 78.5 percent to 72.2 percent and 
 administration's share decreased from 10.5 to 9.5 percent. At the same time the share for industry programs increased 
 from 11.0 to 18.3 percent. 
 
 " Information on advertising expenditures by media type was obtained from an independent marketing research firm 
 (LNA-Mediawatch). Also note that these data are based on a calendar year, while data reported by California marketing 
 order and commission program administrators are based on a California crop year (November through October). 
 
 22 
 
Figure 13. California Avocado Industiy Annual Marketing Expenditures in Millions of Nominal (A) and 
 Real (B) Dollars (1995=100) 
 
 A. Nominal 
 
 12 
 10 
 
 a 6 
 
 1 — I — I — I — I — I — I — I — I — I — I — I — 1 — I — I — I — I — I — I — I — I 
 
 1962 1966 1970 1974 1978 1982 
 
 Year 
 
 -I — I — I — 1 — I — I — I — r 
 
 1986 1990 
 
 1994 
 
 B. Real 
 
 12 
 
 -I — I — I — I — 1—1 — r 
 
 1978 1982 
 Year 
 
 -1 — 1 — r 
 
 1986 
 
 T 1 1 
 
 1994 
 
 -I — I — 1 — I — I 
 
 1962 1966 
 
 I — I — r- 
 
 1970 
 
 1974 
 
 1990 
 
 23 
 
Figure 14. California Avocado Industry Annual Marketing Expenditures by Category, 1986-95 
 
 100% 
 
 80% -■ 
 
 o 
 E- 
 
 60% 
 
 40% 
 
 20% 
 
 ■ Other 
 
 Trade Adv. & Promo. 
 I Consumer Promotion 
 ^ Consumer Advertising 
 
 1986 1988 1990 1992 1994 
 
 Figure 15. California Avocado Industry Annual Advertising by Media Type, 1976-94 
 
 100% 
 
 53 40% 
 
 20% 
 
 0% 
 
 
 Outdoor 
 
 □ 
 
 Radio 
 
 ■ 
 
 Television 
 
 ■ 
 
 Newspapers 
 
 □ 
 
 Magazines 
 
 1976 
 
 1978 
 
 1980 
 
 1982 
 
 1984 1986 
 
 Year 
 
 1988 
 
 1990 
 
 1992 
 
 1994 
 
 merited with other media, varying hmd allocation 
 among television, newspapers, outdoor displays, and 
 radio from year to year. However, in most years media 
 other than magazines accovmted for only small portions 
 of the advertising effort; a notable exception is the tele- 
 vision campaign of 1987 which accounted for over 75 
 percent of advertising funds that year. 
 
 While the effectiveness of generic avocado 
 marketing efforts may depend on the specific program 
 
 implemented, a thorough analysis of the issue requires 
 more detailed data than that considered here. Figures 
 14 and 15 imply that there are not enough 
 disaggregated annual observations to isolate the 
 effects of different types of marketing expenditures. 
 Hence, in this report we examine the effects of all 
 marketing expenditures together and refer to them 
 simply as "advertising" or "advertising and promotion". 
 
 24 
 
AN ECONOMETRIC MODEL OF ANNUAL AVOCADO DEMAND 
 
 In this section we formalize the basic concepts dis- 
 cussed above by developing and testing an economet- 
 ric model of annual avocado demand. The complete 
 set of data used to model the demand relationship is 
 given in Appendix Table 6. As shovm, the variables 
 consist of 34 armual observations from 1962 through 
 1995 corresponding to the factors described above. 
 
 Model Specification 
 
 Specification of the empirical demand model must 
 be based on theory, data availability, and statistical 
 feasibility. Given these considerations, the annual de- 
 mand for avocados was assimied to be represented 
 by the following general function: 
 
 Pc, = f(Qc^Qf,Qm^Y,A,) (7) 
 
 where for a specific year t, Pc,is the farm-gate price of 
 California avocados, Qc, is total sales of California 
 avocados, Qf,is total sales of Florida avocados, Qm,is 
 total avocado imports, Y^is disposable income, and A, 
 is advertising expenditures. Precise definitions and 
 descriptive statistics for each variable are given in 
 Table 5. We foUow the accepted practice of assuming 
 demand is homogeneous of degree zero in money in- 
 come and prices; hence, all doUar-denominated vari- 
 ables are expressed in real terms using the consvimer 
 price index as a deflator." In addition, the quantity 
 variables and income are given in per capita terms to 
 account for the effect of population on demand. The 
 advertising variable is expressed in real terms but is 
 not adjusted for population growth.^^ 
 
 In many market models, prices and quantities 
 traded are determined jointiy through the simulta- 
 neous interaction of supply and demand fimctions. 
 In this case, quantity is said to be an endogenous vari- 
 able, meaning that it does not contribute to the expla- 
 nation of the price level independently but rather is 
 
 something to be explained together with price by the 
 economic model. If an endogenous variable appears 
 on the right-hand side of a regression equation, spe- 
 cial estimation techniques must be used to account 
 for the fact that it is not an independent explanatory 
 factor. In the case of annual avocado demand, how- 
 ever, we assvtme that current-period CaUforrua and 
 Florida quantities are determined by prior-period pro- 
 duction decisior\s and events independent of the cur- 
 rent-period price. Thus, they are predetermined, rather 
 than endogenous, variables. 
 
 On the other hand, the quantity of imports may 
 well depend on the current-period price. It is reason- 
 able to suppose that while foreign production is pre- 
 determined, a larger share enters the country when 
 the domestic price is high and, hence, that Qm, is en- 
 dogenous. Nevertheless, it is possible that the statis- 
 tical consequences of treating a theoretically 
 endogenous variable as if it were an independent ex- 
 planatory factor (exogenous) are negligible. Because 
 the quantity of imports is very small relative to do- 
 mestic avocado production, our strategy is to regard 
 it as an exogenous variable and then perform tests to 
 ascertain whether this simplification is likely to have 
 important effects on the results. 
 
 Finally, since the funds available for advertising 
 are based on producer revenue, and since producer 
 revenue depends on price, the A^ variable might also 
 be endogenous. This did not appear to be a problem 
 in the present cinalysis. As alluded to above, our in- 
 vestigation foimd advertising expenditures to bear 
 some relationship to the previous year's revenues, but 
 very little to the current year's revenues — i.e., was 
 associated with Pc,.j rather than Pc,. In addition, the 
 statistical relationship between advertising expendi- 
 tures and producer revenues was diluted by circum- 
 stances: the avocado industry varied the percentage 
 checkoff over time, the proportion of the total assess- 
 
 » Demand is assumed to be homogeneous of degree zero in money income and prices. We convert advertising expendi- 
 tures into real terms using the same deflator. While this is common practice, it is not ideal. We would prefer to deflate 
 the A, variable with an index of the price of advertising, if one were available, so as to obtain a measure of the quantity of 
 advertising. 
 
 « Theoretically the advertising variable should not be adjusted for population if it is more like a type of "public good" than 
 a "private good." A good is called a public good if (1) certain people cannot be excluded from consuming it (nonexclud- 
 able) and (2) one person's consumption does not dimiiiish that available to others (nonrival). Advertising conducted 
 through mass media clearly has features of a public good. On the other hand, advertising has features of a private good 
 when its cost depends on the number of customers reached. We decided the advertising efforts of the Cahfomia avocado 
 industry have been more like a public good than a private good from the point of view of consumers. However, the 
 demand model was also estimated with the A, variable defined in per capita terms and it had little effect on the results. 
 (The authors thank Julian Alston for bringing the private-good versus public-good point to our attention.) 
 
 25 
 
Table 5. Definitions of Variables Used in Annual Demand Model 
 
 Variable Definition Unit Mean Value StDev 
 
 PC. 
 
 average annual producer price of Cabforrua avocados deflated 
 
 r)\i tViP r'OTici iTn*>r ninf^^ I'nHev fr»r all ifomc ^IQRO-RA — ^^^^ 
 
 real cents 
 per pouna 
 
 51.286 
 
 24.834 
 
 
 per capita sales of Cabfomia avocados during the marketing 
 year 
 
 pounds 
 per capita 
 
 1.0120 
 
 0.5888 
 
 
 per capita sales of Florida avocados during the marketing year 
 
 poimds 
 per capita 
 
 0.1862 
 
 0.0705 
 
 
 per capita imports of avocados during the marketing year 
 
 pounds 
 per capita 
 
 0.0327 
 
 0.0484 
 
 \ 
 
 U.S. per capita disposable income deflated by the coristuner 
 price index for aU items (1982-84=100) 
 
 thousands 
 of real dollars 
 
 10.457 
 
 1.7392 
 
 A. 
 
 annual avocado advertising expenditures deflated by the 
 consumer price index for all items (1982-84=100) 
 
 miUions 
 
 of real doUars 
 
 3.7405 
 
 1.7655 
 
 Notes: variables consist of 33 annual observations, 1962-94, based on a California crop year with timing adjustments as per 
 discussion in text; data sources given in Appendix Table 6. 
 
 ment used for advertising varied by year, and there 
 was carryover of funds from year to year. Thus, we 
 assume that the advertising can be treated as an ex- 
 ogenous or predetermined explanatory factor. 
 
 The annual demand model therefore assumes that 
 price is not jointly determined with any of the explana- 
 tory variables, in the same period, and that simulta- 
 neous equations methods are not needed in 
 estimation. Statistical tests of this assumption are dis- 
 cussed below. 
 
 Data Issues 
 
 A potentially serious problem has to do with the 
 compatibility of the various series in the data set. 
 California price, quantity, and advertising data are 
 reported on a California crop year basis (November 
 through October); Florida price and quantity infor- 
 mation is reported on a Florida crop year basis (April 
 through March); the import data are reported on vary- 
 ing annual bases (see the footnotes for Appendix Table 
 6); and the CPI, income, and population data are re- 
 ported on a calendar year basis. Since the demand 
 relationship is defined for a particular time period, 
 these timing mismatches imply an error in model 
 specification. To deal with this complication we first 
 specify that the California crop year is the standard to 
 which the other data should be matched. Since the 
 CPI, income, and population series change rather 
 smoothly from year to year and coincide with the 
 CaHfomia crop year except for two months, they are 
 asstuned to be acceptably matched with the corre- 
 sponding year ending each California season. Thus, 
 the Florida and import data appear to pose the main 
 difficulties. 
 
 Shipments of Florida avocados by month were 
 available for the 1984-85 through 1993-94 crop years. 
 Using this information it was fovmd that, on average, 
 38 percent of Florida shipments in a given Florida crop 
 year coincided with the corresponding California crop 
 year. For example, approximately 61 percent of 
 Florida's 1984-85 shipments occurred between April 
 1 and October 31, 1984, while 39 percent occurred be- 
 tween November 1, 1984, and March 31, 1985 — there- 
 fore, 61 percent coincided with California's 1983-84 
 crop year while 39 percent coincided with California's 
 1984-85 season. 
 
 Since the break-down for Florida shipments is not 
 known for seasons prior to 1984-85, three separate 
 Florida quantity variables were considered in the 
 analysis. The first Florida quantity variable matches 
 California and Florida observations according to the 
 stated year; e.g., the Horida 1994-95 data is matched 
 with California 1994-95 data. The second Florida 
 quantity variable matches each Florida observation 
 with the stated prior-year Cabfomia observation; e.g., 
 Florida 1994-95 data is matched with California 1993- 
 94 data. For a third option we constructed a new 
 Florida quantity variable by attempting to divide each 
 observation on Florida production into some that be- 
 longs with the current and a remainder that belongs 
 with the prior California crop year. This can be done 
 precisely for those years in which monthly data are 
 available (1984-85 through 1993-94). For other years 
 the percentage of each Florida observation belonging 
 with the corresponding California observation was 
 estimated by averaging the observed percentages in 
 the 1984-85 through 1993-94 period; thus, 38 percent 
 of each Florida observation was assimaed to match 
 with the corresponding Calif orrua crop year, while the 
 
 26 
 
remainder was assumed to match with the prior Cali- 
 fornia crop year. Note that one observation is lost with 
 the second and third options. 
 
 All three Florida quantity variables were used 
 in the econometric model development. Surpris- 
 ingly, the main conclusions did not change when 
 different Florida quantity variables were used. 
 Econometric results presented below are based on 
 the second option (each Florida observation is 
 matched with the stated prior-year California ob- 
 servation). It is important to note that each of the 
 Florida quantity variables suffers from the errors- 
 in-variables problem, which can have potentially 
 serious implications for reliable econometric esti- 
 mation. A test for the statistical importance of this 
 difficulty is described below. 
 
 The compatibility problem is not as significant 
 for import data because: (1) much of the import data 
 is more closely aligned with the California crop 
 year, and (2) imports were a small portion of total 
 supply in those years in which the reporting year 
 did not line up closely with the California crop year 
 (i.e., prior to 1977). Nevertheless, minor realign- 
 ments were made in the import series using 
 monthly data from 1988 through 1995 and a method 
 similar to that described above for Florida quanti- 
 ties. The adjusted annual import series is believed 
 to coincide with California crop years exactly for 
 the 1989-95 period, very closely for the 1977-88 
 period, and closely enough for practical purposes 
 
 where nimabers in parentheses are t-statistics and the 
 nimibers in brackets are the price flexibilities of de- 
 mand with respect to the corresponding variables, 
 evaluated at the data means. 
 
 The estimated coefficients in Box-Cox models are 
 of little interest since they apply to the transformed 
 
 prior to 1977. It was therefore assumed to be an ac- 
 ceptable variable. 
 
 Model Estimation and Testing 
 
 To estimate the annual demand model, a functional 
 form that characterizes the general relationship de- 
 scribed above must be specified. Since choice of func- 
 tional form can have important impacts on the results 
 (e.g., Alston and Chalf ant, 1991), we began by follow- 
 ing Carman and Green (1993) in specifying an ex- 
 tended Box-Cox model — which implies a quite general 
 fimctional form. Thus, the empirical demand model 
 initially assumed is given by 
 
 Pc; - b„ + b^Qc; + b,Qf; + b3Qm;+ b,Y;+ b^A; + e, (S) 
 
 where for each variable X,, X^* symbolizes the Box- 
 Cox transformation defined as (X,^ -l)/XifX^O and 
 InXj if A, = 0. The £, term is assumed to be a normally 
 distributed random error with zero mean and con- 
 stant variance (8, ~ N(0, a^)). Note that the Box-Cox 
 model encompasses the two most common specifica- 
 tions used in empirical analysis: ^ = 1 implies a linear 
 functional form and X = 0 implies a log-linear func- 
 tional form. 
 
 Using annual data for the crop years ending in 
 1962 through 1994, the parameters of the assumed 
 model. Pi (i = 0 to 5) and 7i, were estimated simulta- 
 neously with a procedure based on a statistical good- 
 ness-of-fit criterion (maximum likelihood), yielding 
 the following results: 
 
 (9) 
 
 rather than the original variables. We therefore re- 
 port the estimated price flexibilities of demand de- 
 fined as the percentage change in price resulting from 
 a one percent change in a given explanatory factor.^* 
 For example, the estimated model implies that a one 
 percent increase in advertising expenditures wiU re- 
 
 Fc; = -15.68 - 5.61Qc; + O.SOQf,' - 2.00Qm; + 5.38Y; + 0.37A; + e, 
 (-2.79) (-18.75) (1.38) (-2.08) (6.04) (1.65) 
 [-1.21] [0.09] [-0.11] [2.89] [0.13] 
 X = 0.39, = 0.94, D.W. = 1.45, 
 
 For the general Box-Cox model, it can be shown that the price flexibility of demand with respect to one of the explaia- 
 tory variables, say x, is given by 
 
 \=^^^ 
 
 where X is the estimated Box-Cox parameter for the dependent variable (y) and X is the estimated Box-Cox parameter 
 associated with explanatory variable x. 
 
 27 
 
suit in an approximately 0.13 percent increase in the 
 price of California avocados, at average levels of price 
 and advertising in the sample data. 
 
 The coefficients on Qc^ Qm^ and have the ex- 
 pected signs and are statistically significant at the 95 
 percent confidence level (or better). The coefficient 
 on Qfj has the "wrong" sign — it was expected to be 
 negative — ^but is not statistically significant. The co- 
 efficient on A, has the expected sign but is not statisti- 
 cally significant. 
 
 The statistic implies that the estimated equa- 
 tion generally fits the data well. However, the D.W. 
 (Durbin- Watson) statistic suggests that the error term 
 may be autocorrelated; if so, it does not satisfy the 
 assimiptions vinder which the equation was estimated 
 and, hence, there is a problem with the model. Since 
 the value of the D.W. statistic is in the inconclusive 
 range, a second test was performed which gave strong 
 
 where, as before, numbers in parentheses are t-statis- 
 tics and the numbers in brackets are the price 
 flexibilities of demand with respect to the correspond- 
 ing variables, evaluated at the data means. 
 
 The first thing to note about these results is that 
 the estimated flexibilities and t-statistics are very close 
 to those estimated with the extended Box-Cox model; 
 thus, our conclusions about both the economic and 
 statistical significance of each explanatory variable are 
 essentially the same. Moreover, the statistic indi- 
 cates that the classical Box-Cox fits the data to basi- 
 cally the same degree as the extended model. In a 
 statistical sense, the key difference between the two 
 models is that autocorrelation does not seem to be a 
 problem with the classical Box-Cox. The D.W. statis- 
 tic implies that the errors are not autocorrelated and 
 the test of Savin and White (1978) confirmed this find- 
 ing." These results suggest that incorrect functional 
 form was the cause of autocorrelation in the extended 
 
 evidence that the regression errors do exhibit a high 
 degree of autocorrelation (Savin and White, 1978).^^ 
 Seemingly autocorrelated errors can be the result of 
 model misspecification (incorrect functional form or 
 omitted explanatory variables) or the errors may ac- 
 tually follow an autoregressive process. The former 
 calls the entire model into question while the latter 
 makes the conventionally calculated t-statistics in- 
 valid. Thus, a remedy was sought. 
 
 Examination of alternative functional forms found 
 that a variation on the originally estimated Box-Cox 
 model performed weU. Rather than all variables be- 
 ing transformed by the "k parameter as in the first re- 
 gression (called the extended Box-Cox model), the 
 alternative model transforms only the dependent vari- 
 able (called the classical Box-Cox model).^* The re- 
 sults from estimating the classical Box-Cox model 
 were: 
 
 (10) 
 
 Box-Cox regression, that the classical Box-Cox corrects 
 the problem, and therefore that the latter is a better 
 model. 
 
 There is a possible difficulty with this conclusion 
 however (which is the reason why we report both 
 models). Contrary to the extended Box-Cox model, 
 the estimated classical Box-Cox function implies that 
 price increases at an increasing rate as the explana- 
 tory variables increase. Thus, as the level of advertis- 
 ing rises, each unit increase results in a greater change 
 in price than did previous imit increases. Clearly, this 
 fimctional form cannot prevail for all levels of adver- 
 tising. Economic theory and common sense prescribe 
 that there must be diminishing marginal returns to 
 advertising after some point and, moreover, that the 
 optimal level of advertising is in the range of dimin- 
 ishing returns. If one assvimes that the avocado in- 
 dustry is in equilibrium and behaving rationally, the 
 extended Box-Cox regression model which exhib- 
 
 Pc,* = 1.91 - 0.53QC, + 0.14Qf, - 1.23Qm,+ 0.nY,+ O.OIA, + 
 (18.37) (-20.22) (0.70) (-2.96) (7.51) (1.53) 
 [-1.33] [0.06] [-0.10] [2.77] [0.13] 
 
 % = -0.23, R2 - 0.95, D.W. = 2.11, 
 
 Following Savin and White (1978) the joint hypothesis of 1 unrestricted and r = 0 (the first-order autocorrelation coeffi- 
 cient) was tested against the alternative of 1 and r both unrestricted yielding r = 0.84 and = 19.13. 
 
 " Note that the classical Box-Cox model is not nested by the extended Box-Cox; hence, choosing between the two cannot 
 be accomplished with straightforward tests on restrictions. Due to computational difficulties, we were unable to esti- 
 mate the Box-Tidwell model, where every variable may have an unique transformation parameter and which therefore 
 encompasses both Box-Cox models described here. 
 
 " The joint hypothesis of A, imrestricted cind p = 0 was tested against the alternative of "k and p both unrestricted yielding 
 p =-0.33 and = 1-04. 
 
 28 
 
Figure 16. Hypothetical Market Response to Advertising 
 
 0 10 20 30 40 50 60 70 80 90 100 
 
 Units of Advertising 
 
 its decreasing returns would be preferred. It is quite 
 possible, however, that the CAC has been operating 
 in the area of increasing returns because of previously 
 limited information on the impact of its advertising 
 on avocado prices. In other words, it may be extremely 
 difficult for a commodity organization such as the 
 CAC to determine the nature of returns to advertis- 
 ing without detailed empirical analysis. 
 
 A common hypothesis in the marketing research 
 literature is that meirket response to advertising is S- 
 shaped (e.g. Little, 1979; LiUen, et al., 1992). The con- 
 cept is illustrated by the hypothetical market response 
 function in Figure 16. Initially the market responds 
 (e.g., price changes) slowly to advertising as low lev- 
 els are applied. For levels of advertising below 50 
 imits, the market responds at an increasing rate; for 
 levels of advertising above 50 units, the market re- 
 sponds at a decreasing rate. By ICQ units, advertising 
 
 has reached a saturation point at which more effort 
 has negligible effects. Thus, the theory asserts that 
 price can exhibit both increasing and decreasing mar- 
 ginal returns over different ranges of advertising. 
 
 Considering our estimation results from this per- 
 spective suggests the possibility that advertising efforts 
 in the California avocado industry over the 1962-94 
 time period were in the range of increasing returns (i.e., 
 corresponding to advertising levels below 50 in Figure 
 16). If this is the case, then the appropriate model is the 
 classical Box-Cox. However, while it may be appro- 
 priate for past levels of advertising, it would not be 
 applicable for higher and higher levels of advertis- 
 ing. In addition, if it is believed that observed adver- 
 tising expenditures are at or near optimal levels, then a 
 regression model exhibiting constant or increasing re- 
 turns is not satisfactory since the optimal advertising 
 level must be in the region of diminishing returns.^ 
 
 ^ Because of our particular interest in the effects of advertising, a third model was estimated which allowed both the 
 dependent variable and the advertising variable to each have unique Box-Cox parameters, while all other variables were 
 restricted to be untransformed (for computational feasibility). Many of the key results were quite similar to the other two 
 models (model-fit statistics, precision of the parameter estimates, values of estimated elasticities at the data means, etc.). 
 However, the estimated Box-Cox parameter associated with the advertising variable was very large (k^ = 31) implying 
 an extremely convex functional form. We rejected the model because of the implausible imphcations of its extreme 
 shape. Nevertheless it is worth noting that there is evidence to suggest that the price function should possibly be more 
 convex with respect to advertising (i.e., exhibit greater increasing marginal retunis) than our current model indicates. 
 Allowing for this increases the statistical sigiuficance of the advertising variable (i.e., yields larger t statistics on A,). In 
 addition, it implies that advertising had generally less economic significance at historical levels (i.e., lower price flexibilities 
 at most observed data points), but would have greater economic significance at higher levels. 
 
 29 
 
Further Tests of the Classical Box-Cox Regression 
 Model 
 
 A number of statistical tests were performed to 
 further examine the adequacy of the classical Box-Cox 
 regression model. We report those results here with- 
 out going into much detail. Note that all tests are con- 
 ditional on the estimated model. 
 
 First, recall from previous discussion that there was 
 some concern about the statistical consequences of 
 Qm^ and A, being, possibly, endogenous factors. A 
 closely related issue is whether the measurement er- 
 ror know to exist in the Qf, variable has important 
 impUcations for the results. These questions were 
 examined by means of the Hausman test (see Kmenta, 
 1986, pp. 365-66 and 717-18). A series of such tests 
 were performed using alternatively constructed in- 
 strumental variables. In aU cases, the results strongly 
 supported our assumption that Qm^ and A^ can be 
 treated as effectively exogenous factors. On the other 
 hand, the results of testing for the seriousness of mea- 
 surement error in Qf, (Florida avocado sales) were 
 mixed. Since nothing more can be done to improve 
 the Qf, variable at this time, we simply acknowledge 
 the fact that measurement error may be a problem. 
 
 A condition of the error term called 
 heteroskedasticity is similar in nature and conse- 
 quences to autocorrelation. Its presence means that 
 the regression error does not have a constant variance 
 across the range of the dependent variable. 
 Heteroskedasticity can be caused by model 
 misspecification or it can be a characteristic of the true 
 data generating process. In either case, corrective 
 measures are necessary. Our econometrics program 
 generates a number of test statistics for 
 heteroskedasticity (e.g., the Goldfeld-Quandt test, the 
 Breusch-Pagan test, Harvey tests, etc.; see any econo- 
 metrics textbook for a description). All were consid- 
 ered and none suggested a problem with 
 heteroskedasticity. 
 
 Along with the specification tests implicitly con- 
 ducted through examination of the error term, the 
 RESET method was used to test the hypothesis that 
 no relevant explanatory variables were omitted from 
 the regression equation (Kmenta, 1986, pp. 452-54). 
 The data did not reject the hypothesis. 
 
 It is important to consider that the economic pro- 
 cesses generating observed data may change fvmda- 
 mentally at certain points in time (i.e., structural 
 change). For example, over the time period covered 
 by the analysis, structural changes may have occurred 
 in the effect of advertising due to such things as ad- 
 vertising media used, the nature of advertising copy, 
 competitive conditions, and consvmier response. For 
 empirical modeling, structural change implies that 
 the appropriate regression coefficients, and possibly 
 
 even functional form, may be different for different 
 time periods. A time-varying parameters model, such 
 as employed by Ward and Myers (1979), can be used 
 to test for the effect of such changes. However, there 
 was no "event" in the avocado industry that pointed 
 to a specific point of potential change. A general test 
 (i.e., a sequential Chow test) suggested the possibil- 
 ity of structural change at or around 1974-75. How- 
 ever, attempts to model the phenomenon using 
 standard approaches produced inconclusive results. 
 
 Finally, we note that it is reasonable to assume that 
 the impacts of advertising extend over some period 
 of time and, hence, that this dynamic response should 
 be accounted for in the regression model. However, 
 determination of the nature and duration of the lagged 
 effect is difficult. Nerlove and Waugh (1961) used an 
 average of advertising expenditures over the ten years 
 preceding year t in their study of orange advertising. 
 More recent research indicates that the carryover ef- 
 fect is probably much shorter than ten years, and may, 
 in fact, be less than one year. Clarke (1976) concluded 
 that "90 percent of the aamulative effect of advertis- 
 ing on sales of mature, frequently purchased, low- 
 priced products occurs within 3 to 9 months of the 
 advertisement." Reynolds, McFaul and Goddard 
 (1991) investigated lagged advertising effects of up to 
 six quarters for Canadian butter and cheese. They 
 found optimum lag orders, determined by minimiz- 
 ing Akaike's Final Prediction Error, of five quarters 
 for cheese and one quarter for butter. Other authors 
 have specified comparatively short lag structures. 
 Kinnucan and Forker (1986) specified a Pascal distri- 
 bution for a "goodwill" variable to capture the im- 
 pact of current and past advertising expenditures and 
 assimned that advertising expenditures contributed to 
 goodwill for only six months. Ward and Dixon (1989) 
 specified a twelve month, second-degree poljmomial 
 lag model on advertising with both ends of the lag 
 structure constrained to zero. Since the estimated 
 model utilized annual data, one would expect littie 
 carryover of advertising effects from one year to the 
 next. As partial verification, a model including lagged 
 effects of advertising was estimated. The estimated 
 coefficients for one and two year lags were not statis- 
 tically different from zero and, thus, variables for 
 lagged effects of advertising were not included in the 
 final model estimated. We do, however, expect to find 
 lagged effects from advertising expenditures when 
 moving to a monthly period of analysis. 
 
 Conclusions 
 
 While there are some theoretical concerns about 
 the implications of increasing returns to advertising, 
 the classical Box-Cox regression is our preferred 
 
 30 
 
model. In most respects the model is statistically sovind 
 and logically consistent. We therefore use the results 
 from the classical Box-Cox regression model to draw 
 inferences about the annual demand for California avo- 
 cados during the period from 1962 through 1995. 
 
 As expected, the model indicates that the quantity 
 of avocados offered on the market is an important 
 explanatory factor, having a precise, negative impact 
 on price. The estimated price flexibility of demand of 
 -1.33 (at the data means) indicates that the price elas- 
 ticity of demand is approximately -0.75, implying avo- 
 cado demand is inelastic as predicted. 
 
 We expected Florida avocados to be competitive 
 with CaUfomia avocados, with increased sales of 
 Florida avocados having a negative impact on the 
 price of California avocados. The coefficient on the 
 quantity of Florida avocados, however, has a positive 
 sign, but is not statistically different from zero. There 
 are at least two possible explanatioris for this outcome. 
 First, it might be the result of the data inconsistencies 
 previously discussed in some detail. Second, it may 
 be that the timing of Rorida shipments makes them 
 sometimes competitive and sometimes complemen- 
 tary with CaUfomia avocados within the same year. 
 For instance, if Florida avocados tend to be in greater 
 supply when California avocados are in relatively low 
 supply, their availability may help keep cortsumers in 
 the habit of purchasing avocados and hence have a 
 complementary effect on California demand. More 
 detailed data are needed to resolve this issue. 
 
 Imports were foimd to have a statistically signifi- 
 cant negative impact on California avocado prices. 
 
 This indicates that foreign avocados compete directly 
 with California avocados for consumer dollars. Dis- 
 posable income was foimd to have a very large and 
 significantly positive impact on avocado prices, indi- 
 cating that avocados are a normal good. 
 
 Finally, the classical Box-Cox regression model in- 
 dicates that advertising has had a positive impact on 
 California avocado prices. While the value of the t- 
 statistic for the estimated advertising coefficient is rela- 
 tively close to the critical value, it is not statistically 
 sigriificant at the usual confidence levels (95 percent 
 or better). This result does not lead to the conclusion 
 that advertising is ineffective. Rather, it implies that, 
 with the available annual data and assimied model, 
 our estimates are not precise enough to conclude with 
 95 percent certainty that the advertising coefficient can 
 not in fact equal zero. We believe that improved data 
 will increase the precision of our estimates of the ef- 
 fects of advertising. 
 
 Because of the data limitations noted above, a sig- 
 nificant effort was made to obtain monthly data on 
 each of the variables examined in the annual model 
 of demand. We were successful in obtaining a com- 
 plete set of monthly observations for the most recent 
 nine years (November 1986 through October 1995). 
 In the next section we specify a monthly demand 
 model for Califonua avocados and use these data to 
 derive estimates of the model parameters. This will 
 provide confirmation of the armual results and it 
 should also improve the precision of some of our esti- 
 mated coefficients.^^ 
 
 2' We alluded to possible problems with the data series on marketing expenditures. These problems include the changing 
 composition and categories of expenditures included over time. For example, administration and marketing research 
 are necessary expenditures, but neither are expected to directly affect demand. The relative importance of these and 
 other similar categories of expenditures change over time, with the result being an advertising variable whose measure- 
 ment is subject to possibly large unexplained variability. 
 
 31 
 
AN ECONOMETRIC MODEL OF MONTHLY AVOCADO DEMAND 
 
 A complete monthly data series for California 
 avocado sales, prices, and advertising/ promotion 
 expenditures was assembled for the nine-year pe- 
 riod 1986-87 through 1994-95. When combined 
 with data on consumer income, quantities of 
 Florida and imported avocados, prices of possibly 
 related goods, and brand advertising, these data 
 can be used to estimate a monthly version of the 
 previously estimated annual demand model for 
 California avocados. 
 
 A monthly analysis of the demand for California 
 avocados, with emphasis on the impact of generic 
 advertising and promotion, offers a number of poten- 
 tial advantages over the just-completed annual analy- 
 sis of demand. First, there should be a definite 
 improvement in the quality of the data and in the pre- 
 cision of the econometric estimates. The monthly data 
 will be for a shorter time period when data collection 
 procedures were improved, they wUl be much more 
 consistent in terms of classification and measurement, 
 and they will provide an increased number of obser- 
 vations. Second, monthly data will permit more de- 
 tailed analysis of issues such as the response to various 
 types of marketing expenditures, and the carryover 
 effects of avocado advertising and promotion. Third, 
 monthly data will facilitate matching variables such 
 as sales from different production areas, that did not 
 match exactly with differing crop and marketing years. 
 In the aimual analysis, it appeared that important re- 
 lationships related to seasonality of supply by pro- 
 duction area, possible seasonal demand, and varietal 
 differences were masked by the annual data. There 
 are, however, possible disadvantages to moving to a 
 shorter time period. The most obvious is that a re- 
 duced range of variation for the independent vari- 
 ables, such as consimier income or prices, may reduce 
 the statistical significance of some estimated coeffi- 
 cients. 
 
 Seasonal Sales and Prices 
 
 Seasonal patterns of avocado production and sales 
 in Califonua and other areas, combined with possible 
 seasonal changes in demand, result in changing prices 
 over the marketing year. Recent seasonal patterns of 
 California avocado sales and prices are shown by the 
 indexes in Figiu'e 17. An index of 1.0 is the monthly 
 average for the 1987 through 1995 calendar years.^ 
 
 As shown, average monthly sales tend to be at or 
 above average for the first eight months of the year 
 (January through August), with peak sales usually 
 occurring in May, and below average for the last four 
 months, with the lowest average monthly sales in No- 
 vember The general, although not perfect, inverse 
 relationship between sales and prices are evident in 
 Figure 17. The lowest average prices typically occur 
 in May, when sales are highest, and the highest aver- 
 age prices tend to be in October, when sales are low, 
 but a month before they are the lowest. Note that the 
 decreasing prices with decreasing sales between Oc- 
 tober and November are associated with a change in 
 the varietal composition of sales. Sales of the Hass 
 variety, which fetches the highest average prices, reach 
 a seasonal low in November just as the sales of the 
 other green skin varieties, which peak in December, 
 are increasing. As noted earlier, the Hass variety now 
 accoimts for over 80 percent of California avocado 
 production. Seasonally, the Hass price is highest in 
 November when sales are lowest, and it tends to be 
 lowest in May, when sales are at their seasonal peak. 
 The inverse relationship between seasonal sales and 
 the price of all other varieties is also evident. Their 
 average price is lowest in December when sales tend 
 to be highest. We hypothesize that there is also sea- 
 sonal variation in the demand for avocados. It is im- 
 portant to incorporate these seasonal movements in 
 the estimated demand relationship for avocados. 
 
 The index of seasonal sales of California avocados for the nine-year period 1987 through 1995 was calcxilated by (1) 
 computing monthly sales for each calendar year, (2) dividing monthly sales by average sales for each calendar year to 
 derive an index of monthly sales for that year, and (3) summing the indexes over the nine-year period and dividing by 
 nine to derive an average monthly index. The seasonal price index was developed using the same steps. 
 
 32 
 
Figure 17. Seasonal Index of California Avocado Sales and Average Price, 1986-1995 
 
MONTHLY DEMAND MODEL SPECIFICATION 
 
 The specification of a monthly demand model for 
 avocados is similar to the specification of annual de- 
 mand, but with minor modifications required to ac- 
 count for time-related differences and more detailed 
 data. Thus, we begin by specifying the general 
 monthly demand relationship: 
 
 Pc, = f (Qc^ Qf,, Qm^ A, , BA, ,Y,, D, ,T,) (11) 
 
 where for a specific month t, Pc, represents the aver- 
 age f .o.b. price of California avocados, Qc, is the cor- 
 responding per capita sales of California avocados, 
 Qf,is per capita sales of Florida avocados, Qm,is per 
 capita avocado imports. A, is advertising expenditures 
 by the California Avocado Commission, BA, is brand 
 advertising expenditures by CaHfomia avocado pack- 
 ers, Y,is per capita disposable income, and P^ repre- 
 sents prices of related goods. The variable D,, which 
 was not present in the annual model, represents a row 
 vector of monthly dvimmy variables, which allows the 
 intercept of the inverse demand function to vary by 
 month." These monthly shift variables accoimt for 
 seasonal differences in demand not captured by the 
 other explanatory variables, including such things as 
 shifts in demand related to temperature or the avail- 
 ability of related goods, differences in the number of 
 days in some months, and possible changes in qual- 
 ity over the season. A time trend variable, T,, was in- 
 cluded to accoxmt for changing demand over time not 
 captured by the other shift variables. 
 
 As in the annual analysis, aU doUar-denominated 
 variables are expressed in real terms using the con- 
 svmner price index as a deflator. In addition, the quan- 
 tity variables and income are given in per capita terms 
 to accovmt for the effect of population on demand. The 
 advertising variable is expressed in real terms but is 
 not adjusted for population growth. 
 
 Data Series 
 
 Table 6 defines and describes the key variables 
 available to estimate equation (11). The core data set 
 consists of nine crop years of monthly observations 
 (108 total observations), beginning November, 1986 
 and extending through October, 1995. A listing of most 
 
 of the data utilized for the monthly analysis is included 
 as Appendix Tables 7 through 9}* As shown in Ap- 
 pendix Table 9, price indexes for goods thought to be 
 related to avocados were available. However, initial 
 investigation and subsequent testing, showed these 
 price indexes had little or no explanatory power in 
 the avocado demand relationship; therefore, variables 
 represented by P^ were dropped from the model. Ini- 
 tial investigation and testing of the brand advertising 
 variable BA, also revealed that it added no explana- 
 tory power in the avocado demand relatioriship and 
 it was deleted from the model. The time trend vari- 
 able, T,, was deleted for the same reason. 
 
 The Advertising Variable 
 
 Marketing expenditures by the California Avocado 
 Commission were available by seven categories for 
 the period November 1985 through October 1995. The 
 categories and their shares of total expenditures are: 
 consumer advertising, (41.6%); consumer promotion, 
 (9.6%); trade advertising and promotion, (24.8%); 
 foodservice, (11.8%); public relations, (6.7%); interna- 
 tional promotion, (4.2%); and processed products, 
 (1.3%).^ Attempts to isolate the separate effects of the 
 seven different types of expenditures yielded disap- 
 pointing results. Initial analysis using all seven cat- 
 egories yielded statistically insignificant coefficients 
 for several categories. This led us to group the seven 
 categories into various sub-categories for further 
 analysis. While the estimated coefficients for consumer 
 advertising and consumer promotion were always 
 positive and generally statistically sigiuficant at high 
 cor\fidence levels, the estimated coefficients for the 
 other categories were not. In fact, the variation in signs 
 and magnitudes of the estimated coefficients from 
 formulation to formulation indicated the possible 
 presence of statistical problems with the separate cat- 
 egories.^* Given the overall objectives of this study 
 and our lack of confidence in estimates for separate 
 categories of marketing expenditures, we aggregated 
 the seven categories into a single variable for adver- 
 tising and promotion. 
 
 ^ Monthly dummy variables equal one if the observation is for the designated month and zero otherwise. 
 " Confidential data on monthly CAC marketing expenditures by category are available directly from the Commission. 
 ^ These percentages differ slightly from those noted with Figure 14 due to an additional year's data. 
 ^ Some possible problems include multicollinearity, errors in classifying expenditures, and differing lag structures for the 
 separate categories. 
 
 34 
 
Table 6. Definitions of Variables Used in Monthly Demand Model 
 
 Variable 
 
 Definitiori 
 
 Uruts 
 
 Mean Value 
 
 StDev 
 
 Pc 
 
 monthly average FOB price for all California avocados, 
 deflated by the consumer price index (1982-84=100) 
 
 real cents 
 per poimd 
 
 63.81 
 
 28.36 
 
 Qc 
 
 monthly shipments of all California avocados, 
 divided by US population for the period 
 
 poimds 
 per capita 
 
 0.0989 
 
 0.0435 
 
 
 monthly shipments of all Florida avocados, 
 divided by US population during the period 
 
 poimds 
 per capita 
 
 0.0139 
 
 0.0146 
 
 Qm 
 
 monthly shipments of all imported avocados, 
 divided by US population during the period 
 
 pounds 
 per capita 
 
 0.0089 
 
 0.0159 
 
 MA 
 
 a moving average of monthly CAC expenditures 
 for advertising and promotion, deflated by the 
 consimier price index (1982-84=100) 
 
 milHons of 
 real doUars 
 
 0.3112 
 
 0.1795 
 
 Y 
 
 US per capita disposable income, deflated by the 
 consumer price index for all items (1982-84=100) 
 
 thousands of 
 real dollars 
 
 12.729 
 
 0.2921 
 
 T 
 
 Monthly time trend variable that has a value of 
 one for November 1986 and 108 for October 1995 
 
 month 
 
 
 
 Notes: the core data set consists of 9 years of monthly observations beginning November, 1986 and extending through 
 October, 1995 (108 total observations). 
 
 It is reasonable to expect the effects of advertising 
 and promotion to extend over several weeks or 
 montiis and this dynamic response should be included 
 in the regression model. The challenge is to deter- 
 mine the relevant duration and the nature of these 
 lagged effects. We expect the carryover effect of avo- 
 cado advertising to be less than 12 months, given other 
 research results.^ Because the precise nature and du- 
 ration of the lagged effect is difficult to determine, our 
 strategy was to do initicil model estimation and test- 
 ing with a simple, moving-average lag structure and 
 then expand the analysis of lagged advertising effects 
 after developing a basic model. To that end, a nxmi- 
 ber of moving average processes were considered. Of 
 the alternatives, a three-month moving average, de- 
 fined as: 
 
 MA3. = (A,.j+A,., + A,.3)/3 (12) 
 
 appeared to perform well. Moreover, the lag length 
 implied by MA3, is consistent with related prior work 
 (e.g., Kirmucan and Forker, 1986; Alston, Chalfant, 
 Christian, Meng and Piggott, 1996). This lag struc- 
 ture was therefore used to represent advertising ef- 
 fects in our initial model development and testing. 
 
 The appropriate lag structure is an empirical problem. 
 Thus, after determining the variables to include in the 
 final monthly price equation, we investigated a vari- 
 ety of polynomial lag structures of up to 8 months as 
 a replacement for MA3. 
 
 Model Estimation and Testing 
 
 In the annual model, we argued that crop year to- 
 tal avocado quantities were predetermined by prior- 
 period production decisions and events, such as 
 weather, that were independent of the current-period 
 price. Thus, single equation estimation methods were 
 appropriate. For the monthly analysis, however, it is 
 more likely that prices and sales are determined si- 
 multaneously, since producers may be able to exer- 
 cise some month to month control over harvest and 
 shipment timing, depending on prices. If this is the 
 case, a simultaneous equation system technique, ac- 
 counting for a market process in which prices and 
 quantities are determined jointly through the simul- 
 taneous interaction of supply and demand functions, 
 is required for estimation of monthly demand. 
 
 " These studies by Clarke (1976), Reynolds, McFaul and Goddard (1991), Kinnucan and Forker (1986), and Ward and 
 Dixon (1989) were reviewed earlier. Recall that the annual analysis of avocado demand examined possible advertising 
 lag effects of one and two years, but found that neither was statistically significant. 
 
Simultaneity of Supply and Demand 
 
 The hypothesis that avocado quantities are exog- 
 enous in equation (1) was examined by means of the 
 Hausman test. To conduct the test, a set of instru- 
 mental variables was identified and assembled, con- 
 sisting of the following:^* (1) all of the assumed 
 exogenous variables in the demand equation, includ- 
 ing the monthly dimimy variables; (2) variables indi- 
 cating the total quantities of California and Florida 
 avocados produced in the (respective) crop year cor- 
 responding to each monthly observation;" and (3) 
 lagged values of all endogenous and exogenous vari- 
 ables. The number of potential instrumental variables 
 identified is quite large, since lags of different lengths 
 might be considered. The set was narrowed by re- 
 gressing each quantity variable on a mmiber of po- 
 tential instruments, and selecting those with the 
 greatest explanatory power. (In general, a linear com- 
 bination of good instrumental variables should be 
 highly correlated with the possible endogenous fac- 
 tors.) 
 
 Using different numbers and combinations of in- 
 struments, and different functional forms for f( ), the 
 Hausman test clearly rejected the hypothesis that Qc 
 is exogenous. This result suggests that California avo- 
 cado growers do respond to price in the timing of their 
 harvests and shipments, and the effects show up in 
 data reported on a monthly basis. The results of 
 Hausman tests were somewhat ambiguous concern- 
 ing the quantity variables for Florida and imports (Qf 
 and Qm), depending on the test specification. Never- 
 theless, the majority of the tests rejected exogeniety 
 of both Qf and Qm and we therefore conclude that all 
 quantity variables should be treated as endogenous. 
 
 Statistical Diagnostics and Tests 
 
 The presence of endogenous explanatory variables, 
 which requires the use of simultaneous estimation 
 techniques, substantially complicates the analysis. For 
 instance, their presence means that it is not possible 
 to obtain completely xmbiased estimates of equation 
 (11). It is important to note that the techniques used 
 to estimate and test the model yield approximate re- 
 sults for finite sample sizes, with the accuracy of the 
 approximation increasing with the size of the sample. 
 "ITierefore, our stiategy was to estimate equation (11) 
 in a number of different ways and report those results 
 
 that appear plausible. We are fortunate that having 
 monthly data provides a relatively large sample. 
 
 Equation (11) was estimated with an instrumental 
 variables (IV) method, which gives consistent (i.e., 
 tending toward the true value as the sample size gets 
 larger) parameter estimates. Because functional form 
 tests, such as the Box-Cox described in the annual 
 analysis, are difficult to implement with an IV esti- 
 mation method, we considered four distinct functional 
 forms: (a) linear, (b) log-linear, (c) semi-log (i.e., only 
 the dependent variable tiansformed), and (d) a Box- 
 Cox tiansformation of the dependent variable, using 
 X = -0.23, as estimated for the annual model. 
 
 An important consideration with the IV estima- 
 tion method concerns the ntmiber and set of instru- 
 ments to use. In general, the set of instruments chosen 
 defines a tiadeoff between (finite-sample) bias and 
 efficiency — a larger number of instruments yields 
 more efficient estimates (i.e., estimates with a smaller 
 variance), but also yields estimates that are more bi- 
 ased. In consideration of this fact, all IV regressions 
 and tests were performed with at least two different 
 sets of instruments, one with more instrumental vari- 
 ables and one with fewer, for comparison. A large 
 discrepancy between estimates from the two regres- 
 sions would suggest that the model may have poor 
 finite-sample properties. 
 
 The initial IV regression models were tested for 
 serial correlation and heteroskedasticity with the 
 methods described in Davidson and MacKiimon (pp. 
 369-71 and 560-64, respectively).^ Results were as fol- 
 lows for each fimctional form considered: 
 
 (a) Linear Model: no serial correlation 
 
 significant heteroskedasticity 
 
 (b) Log-linear Model: significant AR(1) errors 
 
 no heteroskedasticity 
 
 (c) Semi-log Model: no serial correlation 
 
 significant heteroskedasticity 
 
 (d) Box-Cox Model: no serial correlation 
 
 significant heteroskedasticity 
 
 Because each model tested positive for one prob- 
 lem or the other, final estimates were obtained with a 
 generalized method of moments technique, a varia- 
 tion on the IV method which is able to accoimt for the 
 indicated problems with the error terms. In the case 
 of heteroskedasticity. White's heteroskedasticity-con- 
 
 ^ In brief, instrumental variables are variables that are known (or believed) to be exogenovis, yet are correlated with the 
 potentially endogenous variables (see, for example, Kennedy, 1992, pp. 136, 159 and 169). 
 
 " These are valid instrumental variables since crop-year-total quantities are assumed to be predetermined. 
 
 ™ Tests were performed for AR(1), AR(2), AR(3), and similar moving average, error processes. 
 
 36 
 
sistent covariance matrix estimator was used to cor- 
 rect for heteroskedasticity (Davidson and MacKinnon, 
 Chapter 17). 
 
 The models were initially estimated with three 
 quantity variables, Qc, Qf and Qm. The estimated 
 coefficient for imports was always much larger than 
 the coefficients for California and Florida quantity. We 
 then tested restrictions on the quantity variables us- 
 ing a method analogous to the standard likelihood 
 ratio test, as described by Newey and West (1987). The 
 hypothesis. Ho: Qc = Qf = Qm, was clearly rejected 
 and the hypothesis. Ho: Qc = Qf, was not rejected. 
 Based on these tests, the final regressions were esti- 
 mated with two quantity variables, Q = Qc + Qf and 
 Qm. 
 
 Estimation Procedure 
 
 Inverse monthly demand equations were esti- 
 mated using each of the specifications discussed 
 above. While the linear and semi-log specifications 
 each provided reasonable parameter values and theo- 
 retically correct signs, the linear model results were 
 statistically superior. Results for the log-linear model 
 were clearly inadequate on at least two counts: first, 
 a number of estimated parameter values were implau- 
 sible, with theoretically incorrect signs; and second, 
 the model was rejected by the J-test of overidentifying 
 restrictions, a test of general model specification for 
 IV regressions.^^ The Box-Cox model results were very 
 similar to those of the semi-log model. 
 
 The final step in estimating the demand function 
 was to determine the appropriate lag structure for the 
 advertising variable. Tlie method used to select the 
 lag structure and formulate the advertising variable 
 follows: 
 
 1. The model was estimated with every combination 
 of polynomial lags of degrees 2, 3 and 4 for lags 
 extending up to eight months. It was clear from 
 the results that longer lags were not appropriate. 
 
 2. For each estimated model, a single weighted-mov- 
 ing-average advertising variable was constructed, 
 with the weights derived from the estimated coef- 
 ficients (normalized to sum to one). 
 
 3. The model was then re-estimated using each of the 
 moving average variables constructed in step 2. 
 The J-statistic for each equation was used as the 
 criterion for selecting the lag length. The J-statis- 
 
 tic is (1 /n) times the optimal value of the criterion 
 function that is minimized to derive the GMM es- 
 timates. It is therefore analogous to SSE and can 
 be used to compare equations as long as every- 
 thing is the same except for the weights used to 
 form the advertising variable. For the equations 
 estimated, the J-statistic was minimized with a 2nd 
 degree polynomial and a lag length of five months. 
 The weights used to construct the advertising vari- 
 able were as follows: 
 
 Month Lag MAS Weig hts 
 
 0 .0000 
 
 1 .0274 
 
 2 .2133 
 
 3 .2996 
 
 4 .2863 
 
 5 .1734 
 
 Estimation Results 
 
 The estimated monthly inverse demand equation 
 for CaHfomia avocados using the above weights for 
 the advertising variable is reported in Table 7. The 
 signs on the coefficients are as expected and most are 
 statistically significant. Since our emphasis is on 
 evaluation of the impact of advertising and promo- 
 tion, we are particularly interested in the estimated 
 advertising coefficient. 
 
 Note that the advertising coefficient is statistically 
 significant at the 95% level.'^ The hnear form of the 
 equation implies the existence of coT\stant returns to 
 advertising; an alternative formulation using the 
 square root of the advertising variable provided simi- 
 lar results but the estimated coefficient was not sig- 
 nificant at the 95% level. The estimated price flexibility 
 of advertising at mean values is 0.137, a value that is 
 very close to the earlier annual estimate of 0.13. This 
 result verifies and strengthens the results from the 
 annual analysis of demand. We conclude that CAC 
 advertising expenditures have increased the demand 
 (and prices) for California avocados. 
 
 There is a strong and statistically significant inverse 
 relationship between monthly sales of domestically 
 produced avocados (CaUfomia plus Florida) and the 
 real f.o.b. price of California avocados. Imported avo- 
 cados substitute for California avocados on a monthly 
 basis, with increased import sales having a negative 
 impact on the price of CaHfomia avocados. Note that 
 
 5' The J-test statistic is distributed as a Chi-squared, with degrees of freedom equal to the number of overidentifying 
 restrictions. 
 
 ^2 An equation using a three-month lagged advertising variable was not selected because of a higher J-Test value (1 .03), but 
 the coefficient on the advertising coefficient was significant at the 2% level. 
 
 37 
 
Table 7. Estimation Results for Monthly California Avocado (Inverse) Demand Model 
 
 Variable 
 
 Coefficients 
 
 Q (CA+FL) 
 
 -872.17* 
 
 
 (-10.42) 
 
 Qm (Imports) 
 
 -1,776.9* 
 
 
 (-5.16) 
 
 MAS 
 
 21.19* 
 
 
 (2.07) 
 
 Y 
 
 2.56 
 
 
 (0.28) 
 
 MD2 
 
 -17.65* 
 
 MD3 
 
 -6.51 
 
 MD4 
 
 -0.37 
 
 MD5 
 
 0.28 
 
 MD6 
 
 8.07 
 
 MD7 
 
 24.65* 
 
 MD8 
 
 30.99* 
 
 MD9 
 
 43.93* 
 
 MDIO 
 
 61.77* 
 
 MDll 
 
 
 MD12 
 
 6.45 
 
 Constant 
 
 122.51 
 
 J-Test 
 
 0.78 
 
 (Overidentifying Restrictions) 
 
 (3) 
 
 Notes: Numbers in parentheses are asymptotic t ratios. To reduce notational clutter, t ratios are not shown for the coeffi- 
 cients associated with the dummy variables and the constant term — asterisks indicate those coefficients that are significant 
 at the 5% level or better. 
 
 for equivalent amounts, the impact of imports is about 
 tw^ice as large as is the impact of domestic avocados. 
 The estimated monthly price flexibilities evaluated at 
 the data means are: CaiLfomia and Florida quantity, 
 -1.54; and imports, -0.25.'' There is also a positive but 
 statistically insignificant relationship between per 
 capita income and the price of California avocados. 
 This lack of significance is not too surprising given 
 the short time period and the small variation in in- 
 come. 
 
 The monthly dimuny variables isolate seasonal 
 changes in demand (and prices) after accounting for 
 
 seasonal patterns of production, imports, and adver- 
 tising. AH of the estimated coefficients measure real 
 price differences from the base of November (the first 
 month of the marketing year). Those coefficients that 
 are significantly different than zero at the 95 percent 
 confidence level include December (MD2), and the five 
 months from May through September (MD7 through 
 MDll). The pattern of monthly shifts in demand is 
 illustrated in Figure 18. As shovra, demand for Cali- 
 fornia avocados is at the seasonal low in December. It 
 then increases rather smoothly to a seasonal high in 
 August and then decreases to the end of the crop year. 
 
 " The comparable annual estimates from prior work were California quantity, -1.33; Florida quantity, 0.06; and imports, 
 -0.10. 
 
 38 
 
Figure 18. Monthly Shifts in California Avocado Demand 
 
 39 
 
ANALYSIS OF ADVERTISING BENEFITS AND COSTS 
 
 There are several approaches available for measur- 
 ing estimated benefits and costs of the California avo- 
 cado industry's advertising programs given the 
 demand and acreage response equations developed 
 in this study. Using either the annual or the monthly 
 demand equations, one can develop short-run (within 
 year) estimates of the benefits of increased demand 
 due to advertising. These estimated annual or 
 monthly benefits are then divided by actual annual 
 or monthly costs to calculate average benefit-cost ra- 
 tios. A ratio greater than one indicates that total re- 
 turns from advertising were greater than the costs; the 
 higher the ratio, the higher are the returns from ad- 
 vertising. A positive benefit-cost ratio less than one 
 indicates that advertising increased revenues but the 
 increase was less than the costs. 
 
 The short-nm benefit-cost ratios based solely on 
 estimated demand do not accoimt for the lagged sup- 
 ply response to short-run price improvements and 
 thus, they tend to overstate the benefits from an ad- 
 vertising program conducted over a long period of 
 time. We combine the acreage response and annual 
 demand equations to form a recursive model of sup- 
 ply response and use this model to simulate annual 
 total revenues over time both with and without the 
 advertising program. The differences in total crop 
 revenues measure advertising benefits which are then 
 compared with aimual program costs. 
 
 The short-run and longer-run benefit-cost ratios 
 outlined above are averages of benefits and costs. The 
 marginal response of revenues to additional adver- 
 tising is also of interest since it indicates whether the 
 industry had over- or imder-allocated funds to adver- 
 tising. We will examine marginal benefit-cost ratios 
 by increasing armual or monthly advertising expen- 
 ditures by one percent and calculating the ratio of the 
 change in benefits to the change in expenditures. 
 
 Analytical Model of Supply and Demand 
 
 The measurement of returns from advertising can 
 be illustrated with the hypothetical supply and de- 
 mand relationships shown in Figure 19. The armual 
 demand curve for avocados without advertising is 
 shown by the demand curve D. With an effective ad- 
 vertising program, the demand curve D will increase 
 (shift to the right) to E>^. Since avocado supply is es- 
 sentially fixed for a given marketing year, as repre- 
 sented by the vertical supply curve S, average prices 
 increase from to Pj . Increased revenues (and prof- 
 its) will encourage producers to expand acreage and, 
 after a lag of several years, production. The lagged 
 
 increase in production is shown by the outward shift 
 of a fixed annual supply from S to S* . Because of 
 sigruficant delays between the time a decision to ex- 
 pand production is made and actual output is avail- 
 able, higher prices to producers will persist during 
 the early years of the advertising program. Then, as 
 new trees reach bearing age, expanded production will 
 shift the vertical supply curve to the right and prices 
 wiU decrease from P^ to P^, as shown in the diagram. 
 As illustrated, total revenue with advertising (the rect- 
 angle OPjCQj) is greater than total revenue without 
 advertising (the rectangle OP(,aQj). 
 
 In this study, the short-run monthly or annual re- 
 turns from advertising are measured by the rectangle 
 PpPjba. The longer run returns from advertising, 
 which account for the effects of supply response, are 
 measured by the difference between total revenue with 
 advertising and total revenue without advertising. 
 The returns from advertising must be compared to 
 the costs of advertising to evaluate the profitability of 
 the program. There are two possible measxires of costs. 
 The obvious measure is the total dollars spent on ad- 
 vertising, which implicitly assumes that avocado pro- 
 ducers bear all of the costs of the program. However, 
 when the advertising cost is financed by a percentage 
 assessment on total crop revenues at the producer 
 level, some of the incidence of the assessment, over 
 time, will fall on buyers through the operation of sup- 
 ply and demand. A second measure of costs allows 
 some of the costs to be borne by buyers, resulting in 
 the producers' share of costs being less than actual 
 expenditures. 
 
 Figure 20 shows the same short-run supply (S) and 
 demand curves D and as in Figiure 19. As noted, 
 the vertical supply curve, representing a fixed supply 
 for a given marketing year, will shift annually due to 
 the lagged effects of new plantings and current remov- 
 als. The long-run supply cxirve can be approximated 
 at any point in time by drawing a line from the initial 
 price-quantity equilibrium through the new price- 
 quantity equilibrium. This is illustrated in the figure 
 by the line LRS connecting the initial equilibrium at 
 point a with a new equilibriimi on the demand curve 
 
 at point c. The function LRS, however, does not 
 include the producer assessment, which when intro- 
 duced, shifts the supply function back to LRS', result- 
 ing in a higher price for buyers (Pj), a lower net price 
 to producers, and a smaller quantity produced and 
 consimied (Qj). 
 
 The amoimt of the price increase depends on the 
 slopes of the supply and demand curves. When 
 supply is fixed and uruBsponsive to price, as it is in 
 
 40 
 
Figure 19. An Economic Model of Avocado Supply and Demand 
 
 the short run, there is no increase in the price to buyers 
 and all of the costs are borne by producers. The more 
 responsive quantity supplied is to price (the more price 
 elastic is supply), tiie sinaller will be the proportion of 
 the assessment borne by producers. Our estimate of 
 
 avocado producers' annual costs for the advertising 
 program over the long run is the difference between 
 total revenue before the assessment and total revenue 
 after the assessment. 
 
 41 
 
AVOCADO SUPPLY RESPONSE 
 
 The price and change in bearing acreage equations 
 estimated in the previous sections were combined into 
 a recursive model of supply response. A diagram of 
 the circular flow of calculated relationships in the 
 model is shown in Figure 21. First, we entered initial 
 values for lagged real total revenue and actual values 
 for per capita disposable income, the consumer price 
 index, the index of prices paid by farmers, popula- 
 tion, average yield per acre, Florida avocado produc- 
 tion, quantity of avocado imports, and advertising 
 expenditures. Starting with these values, the model 
 generated values for annual bearing acreage, average 
 price per pound, and total revenue per acre for Cali- 
 fornia avocados. As illustrated in Figure 21, bearing 
 acreage multiplied by average yield determine Cali- 
 fornia production. Total production is combined with 
 demand factors to calculate the average price of avo- 
 cados. The year-to-year change in bearing acreage was 
 a function of profit expectations, which were based 
 on lagged per acre total revenue (price multiplied by 
 average yield) adjusted by the index of prices paid by 
 farmers, and on avocado prices and the producer cost 
 index lagged one year. 
 
 A comparison of actual and simulated values for 
 bearing acreage is presented in Figure 22. The actual 
 peak bearing acreage was 76,307 in 1987 while the 
 simulated peak was almost identical at 76,289 acres, 
 but it occurred two years earlier in 1985. While the 
 actual and simulated peak acreage are very close, the 
 simulation model typically underestimated or over- 
 estimated acreage during much of the period with the 
 largest overestimated acreage being 5,260 acres in 1980 
 and the largest underestimated acreage being 1,903 
 acres in 1993. The average annual absolute difference 
 between actual and simulated acreage was 1,390 acres 
 (3.6 percent). Overall, the model did a very good job 
 of tracing the total bearing acreage adjustments that 
 occurred during the 1962 through 1995 period, with 
 the difference in actual and simulated 1995 acreage 
 being only 187 acres.^ 
 
 To derive long-nm estimates of the impact of ad- 
 vertising on production and prices of California avo- 
 cados over time, the simulation model was rtm with 
 zero advertising expenditures. 
 
 Comparison of the simulated advertising and no 
 advertising scenarios shows that advertising increased 
 prices and per acre returns and that these improved 
 
 returns led to expansion of bearing acreage and pro- 
 duction. Simulated bearing acreage reaches a peak of 
 76,289 acres in 1985 with advertising and 71,819 acres 
 during the same year without advertising (Appendix 
 Table 10). As shown in Figure 22, the last observation 
 of bearing acreage with zero advertising is 55,196 
 acres, 4,568 acres (almost 8%) less than with advertis- 
 ing. 
 
 Price Elasticity of Supply for California Avocados 
 
 The long-nm supply curve for avocados is an im- 
 portant component for estimating benefit-cost ratios 
 for advertising, but defining the long-run supply curve 
 is difficult because of the extensive lagged relation- 
 ships between production and prices. The long-run 
 industry supply curve shows the production or out- 
 put (number of units) that will be placed on the mar- 
 ket at all alternative prices, other factors equal. In the 
 case of avocados, the supply response to a price change 
 varies by year. This is illustrated in Figure 23 for two 
 scenarios, a one-time, one-shot increase of 10 percent 
 in average annual avocado prices in year zero, and a 
 continuous increase of 10 percent in average armual 
 prices. As shown by the lower Une, the supply re- 
 sponse to a one time 10 percent price increase varies 
 by year over time. There is no change in the quantity 
 supplied until year 3, the maximum response is ap- 
 proximately two percent in year 9, and the effect is 
 negative after year 17 as a result of reduced prices 
 when production was increasing. The supply re- 
 sponse to a continual (permanent) 10 percent price 
 increase is much larger. The maximvmi resporise of 
 approximately 13 percent occurs in year 13 and then 
 the percentage change in output decreases to about 4 
 percent in years 26 to 28 before again increasing. The 
 maximum estimate of price elasticity of supply, which 
 will vary by the year selected, is equal to 1.3. 
 
 Annual Short-Run Benefit-Cost Ratios for Advertising 
 
 The estimated annual demand relatioriship is used 
 to estimate benefits from advertising in a year-to-year 
 (short-run or fixed supply) framework by calculating 
 price and total revenue for actual production each year 
 both with and without advertising. Given the posi- 
 tive coefficient for the advertising variable in the esti- 
 mated demand equation, increased advertising results 
 in higher average prices during a given crop year. 
 
 ^ It is not unusual for models that simulate cumulative values of a parameter to diverge significantly from actual values as 
 the number of periods increase. This model performed much better than is usual for the time period covered. 
 
 42 
 
Figure 21. A Recursive Simulation Model of California Acreage, Production and Prices 
 
 other factors equal. The short-run comparison of an- 
 nual estimated prices with and without advertising 
 indicated that advertising yielded positive net returr\s 
 (the benefit/cost ratio was greater than one) for aU 
 crop years beginning with 1965-66 (Appendix Table 
 11a). The short-run total benefit/cost ratio for adver- 
 tising ranged from 0.76 to 0.93 during the first four 
 years of the program (1961-62 through 1964-65) and 
 then ranged from 1.32 to 14.37 during the remaining 
 period of analysis, with a weighted annual average of 
 7.09 for the total period (Figure 22). Thus, each doUar 
 spent on advertising increased average total sales rev- 
 enue in the same year by $7.09, and after subtracting 
 the cost of advertising, yielded a net return of $6.09. 
 
 Readers will note that some of the largest returns 
 are observed during the most recent eight years of the 
 
 CAC advertising program. Since the costs and ben- 
 efits of the advertising program change from year to 
 year and accrue over time, when calculating returns 
 it is more meaningful to account for changing price 
 relationships and discount the stream of costs and 
 benefits. Thus, the current costs and returns from 
 advertising are restated in 1994-95 doUars in Appen- 
 dix Table 11a, and these are used to calculate the 
 present value of the program at discovmt rates of 0 
 and 3 percent. 
 
 Note that the aimual benefit-cost ratio is the same 
 in current or 1994-95 dollars. When advertising costs 
 and returns are discounted at 0 and 3 percent, the ben- 
 efit-cost ratios are 6.01 and 5.33, respectively. 
 
 This fixed supply analysis does not fully accovmt 
 for the supply response that could occur over time. 
 
 43 
 
Figure 22. California Avocado Bearing Acreage, Actual and Simulated With and Without Advertising, 
 1962-95 
 
 Figure 23. Estimated California Avocado Acreage Response to 10 Percent Price Increase: One-Shot in Year 
 Zero and Permanent 
 
 -2.00 
 
 -4.00 -I — I— I — I — I — I — I — I — I — I — I— 1 — I — I — I — I — I — 1 — I — I — I — I— I — I — I — 1 — I — I — I — I — I — I — I — 1 
 
 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 
 
 Year 
 
 44 
 
Figure 24. Estimated Short-run and Long-run Benefit-Cost Ratios, 1961-62 through 1994-95 Crop Years 
 
 22 
 20 
 18 
 
 O 16 
 ■■S 14 
 
 U 10 
 
 C 6 
 
 01 
 
 0 
 
 Short-run B/C Ratio 
 
 Long-run B/C Ratio 
 
 Long-run B/C Ratio; 
 
 Producers Share 
 
 -IT 
 
 I 
 
 7^ A / I V 
 
 -2 I I ■■ 
 
 1962 1966 
 
 -I — I — I — I — I — r — I — I — I — I — I — I 
 
 1970 1974 1978 
 
 Year 
 
 -I — I — I — I — I — I — n 
 
 1982 1986 
 
 T 1 1 1 1 1 1 
 
 1990 1994 
 
 which may limit the applicability of the weighted av- 
 erage based on summation across the years presented 
 above. The acreages used were those actually occur- 
 ring with advertising, but if advertising had been 
 eliminated in 1975, 1985, or any other year, subsequent 
 with-and-without advertising quantities would have 
 changed. A simple average of the aimual fixed sup- 
 ply benefit-cost ratios avoids the problems of adding 
 up over time. This average, which is equal to 5.25, is 
 close to the estimated aggregate short-term returns 
 from advertising discovmted at 3 percent (B/C = 5.33). 
 
 Monthly Short-Run Benefit-Cost Ratios for Advertising 
 
 Short-run (fixed supply) benefits can also be esti- 
 mated using the monthly demand relationship. Agaia, 
 we calculate the difference in monthly price and total 
 revenue due to advertising for actual monthly sales. 
 Given the positive coefficient on advertising in the 
 estimated demand equation, we know that advertis- 
 ing expenditures increase average prices during a 
 given month, other factors equal. Following the pat- 
 tern used for the annual demand equation, we calcu- 
 lated total monthly benefits and costs associated with 
 CAC marketing expenditiires for the nine crop years 
 1986-87 through 1994-95 on both a current and dis- 
 counted basis. In current terms, the total increase in 
 revenues due to advertising and promotion for the 
 nine-year period was just over $337 million. Total 
 
 CAC marketing expenditixres during the same time 
 period were just over $59 million. Ilius, aggregated 
 benefits and costs yield a benefit/cost ratio of 5.71 for 
 all CAC marketing expenditures dviring the most re- 
 cent nine years. 
 
 To provide a basis of comparison with the annual 
 analysis, the aggregated monthly costs and returns 
 from advertising are restated in October 1995 dollars 
 and these are used to calculate the present value of 
 the program at discovmt rates of 0 and 3 percent. The 
 benefit-cost ratios for all CAC marketing expenditures 
 over the 1986-87 through 1994-95 crop years, when 
 costs and returns are discovmted at 0 and 3 percent, 
 are 5.74 and 6.35, respectively. Note that these dis- 
 counted net returns are close to those estimated for 
 the 1961-62 through 1994-95 crop years using the an- 
 nual demand equation. 
 
 Because of the linear nature of advertising response 
 in the monthly demand equation, the simple average 
 of the monthly benefit-cost ratios and the simple av- 
 erage of monthly marginal benefit-cost ratios is equal.^^ 
 For the nine-year period of analysis, the monthly 
 marginal and average benefit-cost ratios are equal to 
 8.92. The marginal benefit-cost ratios were greater 
 than one for all but two months of the period, indicat- 
 ing that the CAC could have profitably increased ad- 
 vertising and promotion during all but two months 
 of the rune-year period. 
 
 ^ Alston, et al. (1998) derive a simple formula for approximating the benefits from a marginal increase in promotional 
 expenditures. The formula, derived from the commodity demand function, is: where m is the marginal benefit, P is the 
 product price and a is the advertising expenditure. 
 
 45 
 
Long-Run Benefit-Cost Ratios for Advertising 
 
 As documented in the acreage response analysis, 
 producers expand acreage and production when re- 
 turns are favorable and improved returns from ad- 
 vertising expanded the acreage and supply of 
 avocados. The short-run net returns from advertising, 
 described above, were eroded over time by increased 
 supplies stimulated by the earlier increased returns. 
 Given flexible prices (inelastic demand), the increased 
 production from more acres of avocado trees partially 
 offsets the demand-enhancing effects of advertising. 
 As illustrated in Figure 24, the long-rvm benefit-cost 
 ratio was again less than 1.0 during the first four years 
 of the program; then the ratio exceeded one from 1965- 
 66 through 1979-80; the ratio then dropped below 1.0 
 for the next five years and was even negative one year. 
 The estimated benefit-cost ratio was greater than 1.0 
 in 1985-86, and for 7 of 9 years after that. The average 
 benefit-cost ratio for the 34-years of the analysis was 
 1.89; the estimated increase in total industry revenue 
 was $218.8 million and CAC marketing expenditures 
 were $116.0 million. The long-run benefit-cost ratios, 
 when costs and returns are discounted at 0 and 3 per- 
 cent, are 1.78 and 1.71, respectively (Appendix Table 
 lib). A simple average of marginal benefit-cost ratios 
 for the same period, derived by increasing advertis- 
 ing one percent during each year, is equal to 1.48. The 
 marginal ratios tended to be less than one at the be- 
 ginning of the period and greater than one at the end. 
 For example, the average marginal benefit-cost ratio 
 for the first 10 years of the analysis was 0.47 while the 
 average for the last 10 years was 3.23. 
 
 Just as increased demand stimulated increased 
 production of avocados over time, industry assess- 
 ments tended to decrease supply. The long nm im- 
 pact of the adjustment to assessments for 
 advertising is to shift a portion of the costs to buy- 
 ers. Thus, long-rvm benefit-cost ratios estimated 
 above tend to overstate the true costs of the pro- 
 gram to producers. As noted earlier, we compute 
 an estimate of the producers' share of costs by sub- 
 tracting total producer revenue after the assessment 
 from total producer revenue before the assessment. 
 The results of this calculation are shown by year in 
 Appendix Table 11c. The annual average benefit- 
 cost ratio for the 34-years of the analysis was 2.84. 
 The long-run benefit-cost ratios, when costs and re- 
 
 turns are discounted at 0 and 3 percent, are 2.48 and 
 2.26, respectively. 
 
 A Projection of Long-Run Benefit-Cost Ratios for 
 Advertising 
 
 A reviewer correctly noted that the long-run ben- 
 efit/ cost ratios just presented do not accovint for all 
 costs and benefits stemming from the most recent ad- 
 vertising and promotion expenditures. Because of the 
 extensive lagged relationships found in the avocado 
 industry, it is not clear if future adjustments will tend 
 to increase of decrease estimated benefit/ cost ratios. 
 To obtain a measure of the future effects of recent ac- 
 tions, we project industry total revenues both with and 
 without advertising 20 years into the future.'* Then 
 benefit/cost ratios for producers paying aU advertis- 
 ing costs and producers sharing advertising costs with 
 buyers are calculated. As shown in Appendix Table 
 lid, the benefit/ cost ratio when producers pay all of 
 the advertising costs increases from a low of 2.06 in 
 1998 to 5.14 in 2015. The benefit/cost ratio when pro- 
 ducers share the advertising costs with buyers in- 
 creases from a low of 5.02 in 1998 to 7.45 in 2015. Both 
 of these ratios are higher than recent averages, indi- 
 cating that the ratios for the study period did have 
 time to stabilize and that large future costs to recent 
 program actions are not a sigriificant problem. Over- 
 all, the estimated long-nm benefit/cost ratios diiring 
 the study period appear to be on the conservative side, 
 whether the producers pay aU costs or share costs with 
 buyers. 
 
 Thus, returns for CAC advertising and promotion 
 programs have been very attractive, regardless of 
 one's perspective. On a short run, fixed supply, 
 month-to-month and year-to-year basis, returns have 
 typically averaged $5 to $6 for every $1 expended. 
 These are the relevant returns to consider when mak- 
 ing short-run decisioris on CAC advertising and pro- 
 motion program expenditures. Over time, the supply 
 response resulting from increased returns erodes 
 prices and returns. This is the nature of the short-run 
 versus the long-run returns to advertising when the 
 industry does not control supply and there is ease of 
 entry and exit. But, even in the long run, producers 
 grossed over $1.70 for every dollar spent on advertis- 
 ing and promotion since 1961-62. This increased to 
 something over $2.26 for the producers' share of ad- 
 vertising expenditures. 
 
 ^ Future industry developments depend, of course, on the assumptions used for the variables in the simulation model. 
 For this projection we used recent values for advertising ($4.47 million annually), imports (.18685 poimds per capita), 
 Florida production (.1534 poimds per capita), consumer income (increased 1 percent aimually), population (increased 1 
 percent annually), and average per acre yields (6418 pounds per acre). 
 
 46 
 
CONCLUDING COMMENTS 
 
 This report presents the results of research directed 
 toward examination of the effects of CAC advertising 
 and promotion programs on the demand (and price) 
 for California avocados over the period from 1961-62 
 through 1994-95. Aimual demand and supply re- 
 sponse relahoriships were estimated, with generally 
 good results as measured by standard statistical tests 
 and concurrence with theoretical expectations. There 
 were weaknesses with the estimated annual demand 
 equation, however, that appeared to be due to the 
 annual data utilized. The estimated coefficient for the 
 quantity of Florida avocados, for example, was not 
 sigpnificantly different from zero and it had an unex- 
 pected positive sign. As indicated, we beUeve that 
 different marketing and crop years for Califorrua, 
 Florida and imported avocados was an important 
 shortcoming with the data. There were also indica- 
 tions of measurement problems with early advertis- 
 ing and promotion expenditures. We collected and 
 analyzed monthly data for the most recent decade to 
 gain additional information on the nature of the em- 
 pirical demand relationship for CaHf orrua avocados. 
 The results of the monthly analysis generally con- 
 firmed the annual analysis, but with improved statis- 
 tical measures and tests. We found that the effects of 
 Calif orrvia and Florida avocado sales on monthly Cali- 
 fornia prices were essentially the same; we were also 
 able to measure the effect of advertising and promo- 
 tion with increased statistical precision. The similar- 
 ity of estimated annual and monthly price flexibilities 
 of demand makes us very confident that we have been 
 
 able to accurately measure the important determinants 
 of California avocado demand, and in particular, the 
 effects of advertising and promotion expenditures. 
 
 We follow the tradition of empirical economic 
 analyses by noting that more research remains to be 
 done. We were not able, for example, to isolate the 
 separate impacts of various types of advertising and 
 promotion expenditures on the demand and price of 
 California avocados. We believe that carefully de- 
 signed market experiments are required to best assess 
 the comparative impacts of various programs. We 
 were also forced to assume that dollar expenditures 
 are a good measure of advertising efforts; that a dol- 
 lar spent on a given program at a given point in time 
 had the same impact as a doUar spent on any other 
 program at any other time. This measurement prob- 
 lem is common to many studies of commodity adver- 
 tising that utilize secondary data. Monthly data on 
 advertising expenditures were not necessarily 
 matched with the month in which the advertising (or 
 promotion) was commvmicated to the target audience. 
 Future data collection must be aware of the need to 
 match measures of advertising effort with the timing 
 of program execution to derive improved estimates 
 of the dynamic effects of advertising programs. Fi- 
 nally, readers are reminded that the study results are 
 for a specified past time period, and that while most 
 of the estimated supply and demand relationships can 
 reasonably be expected to continue in the near future, 
 any projections using these relationships must be re- 
 garded with caution. 
 
 47 
 
APPENDIX TABLES 
 
 Appendix Table 1. California Avocado Acreage by Category, 1920-1995 
 
 Year 
 
 Bearing 
 
 Nonbearirig 
 
 Total 
 
 Year 
 
 Bearii\g 
 
 Nonbearing 
 
 Total 
 
 
 Acres 
 
 Acres 
 
 Acres 
 
 
 Acres 
 
 Acres 
 
 Acres 
 
 1920 
 
 280 
 
 235 
 
 515 
 
 1958 
 
 19,794 
 
 5,439 
 
 25,233 
 
 1921 
 
 . 310 
 
 289 
 
 599 
 
 1959 
 
 20,205 
 
 5,061 
 
 25,266 
 
 1922 
 
 350 
 
 380 
 
 730 
 
 1960 
 
 21,301 
 
 4,754 
 
 26,055 
 
 1923 
 
 400 
 
 520 
 
 920 
 
 1961 
 
 20,045 
 
 4,378 
 
 24,423 
 
 1924 
 
 450 
 
 809 
 
 1,259 
 
 1962 
 
 20,862 
 
 3,066 
 
 23,928 
 
 1925 
 
 490 
 
 1,382 
 
 1,872 
 
 1963 
 
 21,194 
 
 2,628 
 
 23,822 
 
 1926 
 
 560 
 
 1,789 
 
 2,349 
 
 1964 
 
 21,921 
 
 1,706 
 
 23,627 
 
 1927 
 
 690 
 
 2,437 
 
 3,127 
 
 1965 
 
 21,570 
 
 1,224 
 
 22,794 
 
 1928 
 
 860 
 
 3,599 
 
 4,459 
 
 1966 
 
 18,810 
 
 2,530 
 
 21,340 
 
 1929 
 
 1,210 
 
 4,888 
 
 6,098 
 
 1967 
 
 18,620 
 
 3,060 
 
 21,680 
 
 1930 
 
 1,830 
 
 6,069 
 
 7,899 
 
 1968 
 
 18,730 
 
 3,150 
 
 21,880 
 
 1931 
 
 2,310 
 
 6,550 
 
 8,860 
 
 1969 
 
 19,220 
 
 4,300 
 
 23,520 
 
 1932 
 
 3,040 
 
 8,572 
 
 11,612 
 
 1970 
 
 18,040 
 
 4,200 
 
 22,240 
 
 1933 
 
 4,217 
 
 9,000 
 
 13,217 
 
 1971 
 
 18,380 
 
 4,560 
 
 22,940 
 
 1934 
 
 5,609 
 
 9,196 
 
 14,805 
 
 1972 
 
 19,039 
 
 5,085 
 
 24,124 
 
 1935 
 
 7,307 
 
 7,993 
 
 15,300 
 
 1973 
 
 19,611 
 
 6,029 
 
 25,640 
 
 1936 
 
 8,622 
 
 6,304 
 
 14,926 
 
 1974 
 
 20,741 
 
 6,635 
 
 27,376 
 
 1937 
 
 10,179 
 
 4,097 
 
 14,276 
 
 1975 
 
 20,715 
 
 10,884 
 
 31,599 
 
 1938 
 
 11,226 
 
 3,240 
 
 14,466 
 
 1976 
 
 24,882 
 
 14,692 
 
 39,574 
 
 1939 
 
 11,471 
 
 2,667 
 
 14,138 
 
 1977 
 
 29,041 
 
 14,697 
 
 43,738 
 
 1940 
 
 11,930 
 
 2,541 
 
 14,471 
 
 1978 
 
 33,866 
 
 12,947 
 
 46,813 
 
 1941 
 
 12,132 
 
 2,636 
 
 14,768 
 
 1979 
 
 39,802 
 
 11,335 
 
 51,137 
 
 1942 
 
 12,285 
 
 2,863 
 
 15,148 
 
 1980 
 
 44,369 
 
 11,083 
 
 55,452 
 
 1943 
 
 12,399 
 
 2,995 
 
 15,394 
 
 1981 
 
 47,831 
 
 11,532 
 
 59,363 
 
 1944 
 
 12,756 
 
 2,490 
 
 15,246 
 
 1982 
 
 64,798 
 
 14,808 
 
 79,606 
 
 1945 
 
 13,077 
 
 2,812 
 
 15,889 
 
 1983 
 
 69,448 
 
 12,161 
 
 81,609 
 
 1946 
 
 13,403 
 
 2,884 
 
 16,287 
 
 1984 
 
 72,296 
 
 5,212 
 
 77,508 
 
 1947 
 
 13,565 
 
 3,478 
 
 17,043 
 
 1985 
 
 72,861 
 
 2,208 
 
 75,069 
 
 1948 
 
 12,765 
 
 4,443 
 
 17,208 
 
 1986 
 
 74,131 
 
 1,266 
 
 75,397 
 
 1949 
 
 11,855 
 
 6,254 
 
 18,109 
 
 1987 
 
 74,812 
 
 521 
 
 75,333 
 
 1950 
 
 11,292 
 
 7,131 
 
 18,423 
 
 1988 
 
 76,307 
 
 4,142 
 
 80,449 
 
 1951 
 
 12,008 
 
 8,464 
 
 20,472 
 
 1989 
 
 75,062 
 
 3,083 
 
 78,145 
 
 1952 
 
 12,579 
 
 9,108 
 
 21,687 
 
 1990 
 
 73,368 
 
 2,395 
 
 75,763 
 
 1953 
 
 13,566 
 
 9,135 
 
 22,701 
 
 1991 
 
 71,007 
 
 1,126 
 
 72,133 
 
 1954 
 
 15,040 
 
 8,023 
 
 23,063 
 
 1992 
 
 69,582 
 
 819 
 
 70,401 
 
 1955 
 
 16,292 
 
 6,709 
 
 23,001 
 
 1993 
 
 68,159 
 
 644 
 
 68,803 
 
 1956 
 
 18,036 
 
 5,127 
 
 23,163 
 
 1994 
 
 66,865 
 
 505 
 
 67,370 
 
 1957 
 
 19,119 
 
 5,348 
 
 24,467 
 
 1995 
 
 61,254 
 
 987 
 
 62,241 
 
 Source: Data from 1920 through 1955 are from California Crop and Livestock Reporting Service, Special Publication 261; 
 data from 1956 through 1987 are from California Agricultural Statistics Service, armual issues; data from 1988 forward are 
 from California Avocado Commission. 
 
 48 
 
Appendix Table 2. California Avocado Acreage by County and Area, 1950-1990 
 
 Counties 
 
 
 1 Q<^^ 
 
 
 19f^S 
 
 1970 
 
 1975 
 
 1980 
 
 1985 
 
 1990 
 
 San Diego 
 
 11,474 
 
 13,712 
 
 13,616 
 
 11,798 
 
 12,920 
 
 18,463 
 
 24,254 
 
 36,843 
 
 36,402 
 
 Mid-counties 
 
 
 
 
 
 
 
 
 
 
 Los Angeles 
 
 2,837 
 
 2,754 
 
 2,610 
 
 2,010 
 
 1,260 
 
 517 
 
 986 
 
 615 
 
 602 
 
 Orange 
 
 2,417 
 
 2,617 
 
 2,842 
 
 1,424 
 
 910 
 
 1,136 
 
 2,065 
 
 1,675 
 
 1,471 . 
 
 Riverside 
 
 228 
 
 351 
 
 511 
 
 427 
 
 590 
 
 4,546 
 
 8,737 
 
 8,518 
 
 8,487 
 
 San Bernardino 
 
 71 
 
 87 
 
 179 
 
 120 
 
 110 
 
 151 
 
 136 
 
 126 
 
 220 
 
 Subtotal 
 
 c ceo 
 
 
 
 
 7 870 
 
 6,350 
 
 11,924 
 
 10,934 
 
 10,780 
 
 North Counties 
 
 
 
 
 
 
 
 
 
 
 Santa Barbara 
 
 748 
 
 1,446 
 
 1,646 
 
 2,281 
 
 2,770 
 
 4,369 
 
 6,210 
 
 7,730 
 
 8,029 
 
 San Luis Obispo 
 
 0 
 
 1 
 
 1 
 
 1 
 
 0 
 
 502 
 
 1,049 
 
 804 
 
 1,245 
 
 Ventura 
 
 1,171 
 
 2,179 
 
 2,927 
 
 2,720 
 
 3,460 
 
 8,557 
 
 13,681 
 
 16,596 
 
 16,459 
 
 Subtotal 
 
 1,919 
 
 3,626 
 
 4,5/4 
 
 
 
 
 
 
 95 733 
 
 San Joaquin Valley 
 
 
 
 
 
 
 
 
 
 
 Fresno 
 
 2 
 
 2 
 
 7 
 
 39 
 
 180 
 
 261 
 
 382 
 
 418 
 
 312 
 
 Tulare 
 
 1 
 
 0 
 
 48 
 
 65 
 
 230 
 
 888 
 
 1,658 
 
 1,802 
 
 1,179 
 
 Kern 
 
 0 
 
 0 
 
 0 
 
 0 
 
 0 
 
 113 
 
 127 
 
 173 
 
 14 
 
 Subtotal 
 
 3 
 
 2 
 
 55 
 
 104 
 
 410 
 
 1,262 
 
 2,167 
 
 2393 
 
 1305 
 
 Other 
 
 16 
 
 14 
 
 36 
 
 11 
 
 180 
 
 71 
 
 78 
 
 97 
 
 118 
 
 TOTAL 
 
 18,965 
 
 23,163 
 
 24,423 
 
 20,896 
 
 22,610 
 
 39,574 
 
 59,363 
 
 75,397 
 
 74,538 
 
 Source: California Agricultural Statistics Service, California Fruit and Nut Acreage, Annual Issues. 
 
 49 
 
Appendix Table 3. California Avocado Average Yields per Acre, 1925-1995 
 
 Year 
 
 Ave Yield 
 
 Year 
 
 Ave Yield 
 
 
 
 
 IDS / acre 
 
 1925 
 
 531 
 
 1961 
 
 3542 
 
 1926 
 
 821 
 
 1962 
 
 4793 
 
 1927 
 
 1797 
 
 1963 
 
 3775 
 
 1928 
 
 744 
 
 1964 
 
 4270 
 
 1929 
 
 1901 
 
 1965 
 
 2225 
 
 1930 
 
 437 
 
 1966 
 
 6167 
 
 1931 
 
 1861 
 
 1967 
 
 8002 
 
 1932 
 
 1711 
 
 1968 
 
 3994 
 
 1933 
 
 806 
 
 1969 
 
 6358 
 
 1934 
 
 891 
 
 1970 
 
 3659 
 
 1935 
 
 2546 
 
 1971 
 
 7291 
 
 1936 
 
 1206 
 
 1972 
 
 2731 
 
 1937 
 
 1218 
 
 1973 
 
 7180 
 
 1938 
 
 944 
 
 1974 
 
 5149 
 
 1939 
 
 2598 
 
 1975 
 
 10186 
 
 1940 
 
 1308 
 
 1976 
 
 4694 
 
 1941 
 
 2407 
 
 1977 
 
 8264 
 
 1942 
 
 3028 
 
 1978 
 
 6319 
 
 1943 
 
 2516 
 
 1979 
 
 6181 
 
 1944 
 
 3340 
 
 1^ 
 
 3381 
 
 1945 
 
 1774 
 
 1981 
 
 9952 
 
 1946 
 
 3581 
 
 1982 
 
 4846 
 
 1947 
 
 2728 
 
 1983 
 
 5817 
 
 1948 
 
 2914 
 
 1984 
 
 6833 
 
 1949 
 
 2429 
 
 1985 
 
 5490 
 
 1950 
 
 2745 
 
 1986 
 
 4317 
 
 1951 
 
 3731 
 
 1987 
 
 7432 
 
 1952 
 
 4452 
 
 1988 
 
 4693 
 
 1953 
 
 3420 
 
 1989 
 
 4391 
 
 1954 
 
 2832 
 
 1990 
 
 2834 
 
 1955 
 
 5549 
 
 1991 
 
 3824 
 
 1956 
 
 2218 
 
 1992 
 
 
 1957 
 
 1653 
 
 1993 
 
 8360 
 
 1958 
 
 4678 
 
 1994 
 
 4053 
 
 1959 
 
 5098 
 
 1995 
 
 4966 
 
 1960 
 
 6572 
 
 
 
 Source: Data from 1920 through 1955 are from California Crop and Livestock Reporting Service, Special Publication 261; 
 data from 1956 through 1987 are from California Agricultural Statistics Service, annual issues; data from 1988 forward are 
 from California Avocado Commission. 
 
 50 
 
Appendix Table 4. New California Avocado Plantings Reported the Year of Planting and Up to Eight 
 Later, 1950-1992 
 
 Avocado planting in year t that was standing in year 
 
 
 t 
 
 t+1 
 
 t+2 
 
 t+3 
 
 t+4 
 
 t+D 
 
 t+D 
 
 1+/ 
 
 t+8 
 
 1950 
 
 706 
 
 848 
 
 878 
 
 2023 
 
 2063 
 
 2059 
 
 2257 
 
 2160 
 
 2044 
 
 1951 
 
 688 
 
 772 
 
 1691 
 
 1792 
 
 1714 
 
 1870 
 
 1911 
 
 1843 
 
 1868 
 
 1952 
 
 238 
 
 982 
 
 1436 
 
 1468 
 
 1642 
 
 1838 
 
 1641 
 
 1621 
 
 1522 
 
 1953 
 
 551 
 
 988 
 
 1026 
 
 1175 
 
 1570 
 
 1327 
 
 1375 
 
 1239 
 
 1201 
 
 1954 
 
 417 
 
 517 
 
 896 
 
 1175 
 
 1228 
 
 1212 
 
 1201 
 
 1181 
 
 1112 
 
 1955 
 
 399 
 
 737 
 
 1318 
 
 1435 
 
 1454 
 
 1426 
 
 1420 
 
 1381 
 
 1367 
 
 1956 
 
 897 
 
 1151 
 
 1322 
 
 1469 
 
 1493 
 
 1521 
 
 1529 
 
 1516 
 
 1496 
 
 1957 
 
 224 
 
 452 
 
 616 
 
 1119 
 
 1154 
 
 1159 
 
 1151 
 
 1130 
 
 1015 
 
 1958 
 
 621 
 
 1025 
 
 1037 
 
 1046 
 
 1073 
 
 1072 
 
 1026 
 
 946 
 
 946 
 
 1959 
 
 187 
 
 550 
 
 561 
 
 719 
 
 719 
 
 707 
 
 668 
 
 668 
 
 668 
 
 1960 
 
 178 
 
 192 
 
 392 
 
 391 
 
 392 
 
 404 
 
 442 
 
 428 
 
 428 
 
 1961 
 
 112 
 
 233 
 
 234 
 
 221 
 
 338 
 
 349 
 
 342 
 
 344 
 
 338 
 
 1962 
 
 209 
 
 246 
 
 247 
 
 429 
 
 438 
 
 419 
 
 420 
 
 380 
 
 370 
 
 1963 
 
 97 
 
 149 
 
 390 
 
 486 
 
 512 
 
 533 
 
 490 
 
 486 
 
 486 
 
 1964 
 
 155 
 
 461 
 
 546 
 
 581 
 
 578 
 
 575 
 
 573 
 
 573 
 
 573 
 
 1965 
 
 555 
 
 656 
 
 667 
 
 666 
 
 746 
 
 724 
 
 724 
 
 787 
 
 786 
 
 1966 
 
 702 
 
 853 
 
 871 
 
 906 
 
 913 
 
 924 
 
 1041 
 
 1042 
 
 1023 
 
 1967 
 
 453 
 
 504 
 
 1000 
 
 1000 
 
 978 
 
 1006 
 
 996 
 
 978 
 
 1011 
 
 1968 
 
 441 
 
 1024 
 
 1039 
 
 1053 
 
 1072 
 
 1036 
 
 1279 
 
 1458 
 
 1495 
 
 1969 
 
 498 
 
 532 
 
 617 
 
 672 
 
 725 
 
 638 
 
 830 
 
 867 
 
 864 
 
 1970 
 
 713 
 
 955 
 
 1222 
 
 1294 
 
 1211 
 
 1266 
 
 1397 
 
 1297 
 
 1331 
 
 1971 
 
 1344 
 
 1674 
 
 1753 
 
 1656 
 
 2219 
 
 2308 
 
 2335 
 
 2284 
 
 2290 
 
 1972 
 
 1234 
 
 1477 
 
 1665 
 
 2475 
 
 2632 
 
 2671 
 
 2611 
 
 2617 
 
 2648 
 
 1973 
 
 1386 
 
 2607 
 
 3632 
 
 3816 
 
 3780 
 
 3952 
 
 3944 
 
 3960 
 
 5949 
 
 1974 
 
 3745 
 
 5593 
 
 5764 
 
 5945 
 
 6373 
 
 6384 
 
 6415 
 
 8467 
 
 8333 
 
 1975 
 
 5078 
 
 4451 
 
 4398 
 
 5197 
 
 5255 
 
 5298 
 
 8104 
 
 7534 
 
 6669 
 
 1976 
 
 3782 
 
 3949 
 
 3921 
 
 3878 
 
 4036 
 
 6120 
 
 6284 
 
 6652 
 
 6588 
 
 1977 
 
 3197 
 
 2930 
 
 2996 
 
 3065 
 
 4962 
 
 5274 
 
 5940 
 
 5904 
 
 5884 
 
 1978 
 
 2971 
 
 3648 
 
 3689 
 
 5125 
 
 5616 
 
 6063 
 
 5925 
 
 5940 
 
 5930 
 
 1979 
 
 3629 
 
 3692 
 
 5345 
 
 5308 
 
 5421 
 
 5352 
 
 5386 
 
 5560 
 
 5462 
 
 1980 
 
 3636 
 
 4556 
 
 4629 
 
 4026 
 
 4033 
 
 4059 
 
 4198 
 
 4159 
 
 4108 
 
 1981 
 
 4305 
 
 3948 
 
 2974 
 
 2967 
 
 3039 
 
 3035 
 
 2886 
 
 2891 
 
 3028 
 
 1982 
 
 2933 
 
 1109 
 
 1107 
 
 1143 
 
 1202 
 
 1435 
 
 1465 
 
 1618 
 
 1619 
 
 1983 
 
 488 
 
 537 
 
 701 
 
 737 
 
 848 
 
 861 
 
 880 
 
 924 
 
 9/4 
 
 1984 
 
 109 
 
 293 
 
 287 
 
 360 
 
 368 
 
 420 
 
 449 
 
 454 
 
 A CC 
 
 1985 
 
 13 
 
 38 
 
 121 
 
 127 
 
 203 
 
 355 
 
 357 
 
 431 
 
 
 1986 
 
 23 
 
 321 
 
 342 
 
 392 
 
 603 
 
 621 
 
 610 
 
 
 
 1987 
 
 61 
 
 78 
 
 130 
 
 149 
 
 162 
 
 204 
 
 
 
 
 1988 
 
 18 
 
 88 
 
 226 
 
 232 
 
 247 
 
 
 
 
 
 1989 
 
 57 
 
 124 
 
 130 
 
 130 
 
 
 
 
 
 
 1990 
 
 29 
 
 43 
 
 60 
 
 
 
 
 
 
 
 1991 
 
 
 55 
 
 
 
 
 
 
 
 
 1992 
 
 1 
 
 
 
 
 
 
 
 
 
 Note: Bold-faced entries in the table are the maximum acres reported as being planted in year t; source: California Agricul- 
 tural Statistics Service, California Fruit & Nut Acreage, Annual Issues. 
 
 51 
 
Appendix Table 5. California Avocado Acreage by Category, Plantings and Removals, 1950-92 
 
 Year 
 
 T>1 . l1 ^ 
 
 1 lantmg 
 
 Bearing Acres 
 
 NonBearing Acres 
 
 Total Acres 
 
 Removals 
 
 1950-51 
 
 2257 
 
 12008 
 
 8464 
 
 20472 
 
 1042 
 
 1952 
 
 1911 
 
 12579 
 
 9108 
 
 21687 
 
 897 
 
 1953 
 
 1838 
 
 13566 
 
 9135 
 
 22701 
 
 1476 
 
 1954 
 
 1570 
 
 15040 
 
 8023 
 
 23063 
 
 1632 
 
 1955 
 
 1228 
 
 16292 
 
 6709 
 
 23001 
 
 1066 
 
 1956 
 
 1454 
 
 18036 
 
 5127 
 
 23163 
 
 150 
 
 1957 
 
 1529 
 
 19119 
 
 5348 
 
 24467 
 
 763 
 
 1958 
 
 1159 
 
 19794 
 
 5439 
 
 25233 
 
 1126 
 
 1959 
 
 1073 
 
 20205 
 
 5061 
 
 25266 
 
 284 
 
 1960 
 
 719 
 
 21301 
 
 4754 
 
 26055 
 
 2351 
 
 1961 
 
 442 
 
 20045 
 
 4378 
 
 24423 
 
 937 
 
 1962 
 
 349 
 
 20862 
 
 3066 
 
 23928 
 
 455 
 
 1963 
 
 438 
 
 21194 
 
 2628 
 
 23822 
 
 633 
 
 1964 
 
 533 
 
 21921 
 
 1706 
 
 23627 
 
 1362 
 
 1965 
 
 581 
 
 21574 
 
 1224 
 
 22798 
 
 2039 
 
 1966 
 
 787 
 
 18810 
 
 2530 
 
 21340 
 
 447 
 
 1967 
 
 1042 
 
 18620 
 
 3060 
 
 21680 
 
 842 
 
 1968 
 
 1011 
 
 18730 
 
 3150 
 
 21880 
 
 -629 
 
 1969 
 
 1495 
 
 19220 
 
 4300 
 
 23520 
 
 2775 
 
 1970 
 
 867 
 
 18040 
 
 4200 
 
 22240 
 
 167 
 
 1971 
 
 1397 
 
 18380 
 
 4560 
 
 22940 
 
 213 
 
 1972 
 
 2335 
 
 19039 
 
 5085 
 
 24124 
 
 819 
 
 1973 
 
 2671 
 
 19611 
 
 6029 
 
 25640 
 
 935 
 
 1974 
 
 5949 
 
 20741 
 
 6635 
 
 27376 
 
 1726 
 
 1975 
 
 8467 
 
 20715 
 
 10884 
 
 31599 
 
 492 
 
 1976 
 
 8104 
 
 24882 
 
 14692 
 
 39574 
 
 3940 
 
 1977 
 
 6652 
 
 29041 
 
 14697 
 
 43738 
 
 3577 
 
 1978 
 
 5940 
 
 33866 
 
 12947 
 
 46813 
 
 1616 
 
 1979 
 
 6063 
 
 39802 
 
 11335 
 
 51137 
 
 1748 
 
 1980 
 
 5560 
 
 44369 
 
 11083 
 
 55452 
 
 1649 
 
 1981 
 
 4629 
 
 47831 
 
 11532 
 
 59363 
 
 -15614 
 
 1982 
 
 4305 
 
 64798 
 
 14808 
 
 79606 
 
 2302 
 
 1983 
 
 2933 
 
 69448 
 
 12161 
 
 81609 
 
 7034 
 
 1984 
 
 924 
 
 72296 
 
 5212 
 
 77508 
 
 3363 
 
 1985 
 
 455 
 
 72861 
 
 2208 
 
 75069 
 
 127 
 
 1986 
 
 431 
 
 74131 
 
 1266 
 
 75397 
 
 495 
 
 1987 
 
 621 
 
 74812 
 
 521 
 
 75333 
 
 -4495 
 
 1988 
 
 204 
 
 76307 
 
 4142 
 
 80449 
 
 2508 
 
 1989 
 
 247 
 
 75062 
 
 3083 
 
 78145 
 
 2629 
 
 1990 
 
 130 
 
 73368 
 
 2395 
 
 75763 
 
 3760 
 
 1991 
 
 60 
 
 71007 
 
 1126 
 
 72133 
 
 1792 
 
 1992 
 
 55 
 
 69582 
 
 819 
 
 70401 
 
 1653 
 
 1993 
 
 1 
 
 68159 
 
 644 
 
 68803 
 
 1434 
 
 Source: Plantings are from Appendix Table 4; Bearing and Nonbearing acres are from CASS; Removals are calculated from 
 the other series using the procedures described in the text. 
 
 52 
 
Appendix Table 6. Data Used in Annual Avocado Demand Model 
 
 
 
 
 US 
 
 Per Capita 
 
 California 
 
 California 
 
 
 
 CPI 
 
 Population 
 
 Income 
 
 Price 
 
 Production 
 
 Obs 
 
 Year 
 
 (1982-84=100) 
 
 (millions) 
 
 ($l,000s) 
 
 (cents/poiind) (million poimds) 
 
 
 
 0.267 
 
 159.6 
 
 1.58 
 
 17.40 
 
 46.40 
 
 _7 
 
 
 0.269 
 
 162.4 
 
 1.59 
 
 18.50 
 
 42.60 
 
 ■V 
 
 J. ^'JoJ 
 
 0.268 
 
 165.3 
 
 1.67 
 
 10.45 
 
 90.40 
 
 _C 
 
 "O 
 
 1Q56 
 
 0.272 
 
 168.2 
 
 1.74 
 
 20.70 
 
 40.00 
 
 A 
 
 
 0.281 
 
 ■ 171.3 
 
 1.80 
 
 22.00 
 
 31.60 
 
 
 
 0.289 
 
 174.1 
 
 1.83 
 
 9.70 
 
 92.60 
 
 
 
 0.291 
 
 177.1 
 
 1.97 
 
 8.40 
 
 103.00 
 
 
 X7Uv 
 
 0.296 
 
 180.8 
 
 2.01 
 
 5.50 
 
 140.00 
 
 
 1Q61 
 
 0.299 
 
 183.7 
 
 2.06 
 
 14.00 
 
 71.00 
 
 1 
 
 X 
 
 1962 
 
 0.302 
 
 186.6 
 
 2.15 
 
 10.70 
 
 100.00 
 
 
 1963 
 
 0.306 
 
 189.3 
 
 2.23 
 
 13.40 
 
 80.00 
 
 
 1964 
 
 0.310 
 
 191.9 
 
 2.38 
 
 12.90 
 
 93.60 
 
 J. 
 
 
 0.315 
 
 194.3 
 
 2.54 
 
 26.00 
 
 48.00 
 
 c 
 
 X7DD 
 
 0.324 
 
 196.6 
 
 2.72 
 
 13.10 
 
 116.00 
 
 o 
 
 
 0.334 
 
 198.8 
 
 2.88 
 
 10.10 
 
 149.00 
 
 7 
 
 X700 
 
 0.348 
 
 200.7 
 
 3.10 
 
 23.00 
 
 74.80 
 
 Q 
 O 
 
 X7U7 
 
 0.367 
 
 202.7 
 
 3.30 
 
 15.00 
 
 122.20 
 
 g 
 
 1970 
 
 0.388 
 
 205.1 
 
 3.55 
 
 34.10 
 
 66.00 
 
 10 
 
 1971 
 
 X X 
 
 0.405 
 
 207.7 
 
 3.81 
 
 18.80 
 
 134.00 
 
 11 
 XX 
 
 1972 
 
 X7/ 
 
 0.418 
 
 209.9 
 
 4.07 
 
 47.60 
 
 52.00 
 
 12 
 
 1973 
 
 0.444 
 
 211.9 
 
 4.55 
 
 27.30 
 
 140.80 
 
 Xt7 
 
 1974 
 
 0.493 
 
 213.9 
 
 4.93 
 
 39.60 
 
 106.80 
 
 Xrl 
 
 1975 
 
 X7/ — ' 
 
 0.538 
 
 216.0 
 
 5.37 
 
 23.80 
 
 211.00 
 
 ic; 
 
 xc 
 
 1976 
 
 0.569 
 
 218.1 
 
 5.84 
 
 51.90 
 
 116.80 
 
 
 1977 
 
 0.606 
 
 220.3 
 
 6.36 
 
 29.70 
 
 240.00 
 
 17 
 
 X/ 
 
 1978 
 
 0.652 
 
 222.6 
 
 7.10 
 
 37.00 
 
 214.00 
 
 IK 
 
 1979 
 
 0.726 
 
 225-1 
 
 7.86 
 
 34.60 
 
 246.00 
 
 
 1980 
 
 0.824 
 
 227.7 
 
 8.67 
 
 74.80 
 
 150.00 
 
 
 1981 
 
 0.909 
 
 230.0 
 
 9.57 
 
 17.90 
 
 476.00 
 
 21 
 
 1982 
 
 0.965 
 
 232.2 
 
 10.11 
 
 34.50 
 
 314.00 
 
 
 1983 
 
 0.996 
 
 234.3 
 
 10.76 
 
 23.00 
 
 404.00 
 
 
 
 1.039 
 
 236.4 
 
 11.89 
 
 18.50 
 
 494.00 
 
 9A 
 Z4 
 
 
 1.076 
 
 238.5 
 
 12.59 
 
 29.10 
 
 400.00 
 
 
 
 1.096 
 
 240.7 
 
 13.24 
 
 50.80 
 
 320.00 
 
 
 1 QR7 
 
 1 136 
 
 242.8 
 
 13.85 
 
 16.90 
 
 556.00 
 
 Z/ 
 
 1 QH8 
 I7O0 
 
 X.XOv/ 
 
 245.1 
 
 14.86 
 
 57.00 
 
 358.10 
 
 
 
 1.240 
 
 247.4 
 
 15.74 
 
 62.80 
 
 329.60 
 
 29 
 
 1990 
 
 1.307 
 
 250.0 
 
 16.67 
 
 114.20 
 
 207.90 
 
 30 
 
 1991 
 
 1.362 
 
 252.7 
 
 17.19 
 
 71.20 
 
 271.50 
 
 31 
 
 1992 
 
 1.403 
 
 255.4 
 
 18.06 
 
 58.70 
 
 310.90 
 
 32 
 
 1993 
 
 1.445 
 
 258.2 
 
 18.55 
 
 20.70 
 
 569.80 
 
 33 
 
 1994 
 
 1.482 
 
 260.7 
 
 19.25 
 
 92.70 
 
 271.00 
 
 34 
 
 1995 
 
 1.524 
 
 263.1 
 
 20.17 
 
 74.70 
 
 304.20 
 
 Notes: CPI, population and income are reported on a calendar year; source: The Economic Report of the President and recent 
 issues of 77k Survey of Current Business. California prices and production quantities are reported on a California crop year 
 (year ending October 31 of stated year); source: California Agricultural Statistics Service. 
 
 53 
 
Appendix Table 6 (continued). Data Used in Annual Avocado Demand Model 
 
 
 
 CAC Marketing 
 
 Florida 
 
 Florida 
 
 
 
 
 
 Expenditures 
 
 Price 
 
 Production 
 
 Imports 
 
 Exports 
 
 Obs 
 
 Year 
 
 (million $) 
 
 (cents /pound) (million pounds) (million pounds) (million pounds) 
 
 -8 
 
 1953 
 
 0.00 
 
 5.50 
 
 19.14 
 
 6.92 
 
 
 -7 
 
 1954 
 
 0.00 
 
 4.90 
 
 23.32 
 
 8.28 
 
 
 -6 
 
 1955 
 
 0.00 
 
 5.10 
 
 25.96 
 
 7.40 
 
 
 -5 
 
 1956 
 
 0.00 
 
 5.10 
 
 28.98 
 
 5.34 
 
 
 -4 
 
 1957 
 
 0.00 
 
 5.60 
 
 21.23 
 
 6.61 
 
 
 -3 
 
 1958 
 
 0.00 
 
 4.80 
 
 27.66 
 
 5.72 
 
 
 -2 
 
 1959 
 
 0.00 
 
 6.30 
 
 8.14 
 
 7.07 
 
 
 -1 
 
 1960 
 
 0.00 
 
 4.00 
 
 15.51 
 
 8.77 
 
 
 0 
 
 1961 
 
 0.00 
 
 8.00 
 
 3.96 
 
 6.14 
 
 
 1 
 
 1962 
 
 0.18 
 
 7.70 
 
 13.40 
 
 0.17 
 
 
 2 
 
 1963 
 
 0.44 
 
 6.40 
 
 25.70 
 
 0.03 
 
 
 3 
 
 1964 
 
 0.51 
 
 6.40 
 
 30.60 
 
 0.01 
 
 
 4 
 
 1965 
 
 0.32 
 
 8.20 
 
 28.00 
 
 0.07 
 
 
 5 
 
 1966 
 
 0.79 
 
 20.40 
 
 6.20 
 
 0.25 
 
 
 
 1967 
 
 0.78 
 
 10.20 
 
 12.80 
 
 0.59 
 
 
 7 
 
 1968 
 
 0.58 
 
 8.50 
 
 32.30 
 
 0.37 
 
 
 g 
 
 1969 
 
 0.79 
 
 10.80 
 
 27.70 
 
 0.20 
 
 
 9 
 
 1970 
 
 0.76 
 
 13.10 
 
 30.80 
 
 0.80 
 
 
 10 
 
 1971 
 
 1.21 
 
 14.50 
 
 41.40 
 
 1.14 
 
 
 11 
 
 1972 
 
 1.10 
 
 16.90 
 
 42.50 
 
 2.26 
 
 
 12 
 
 1973 
 
 1.29 
 
 16.10 
 
 41.40 
 
 1.95 
 
 
 13 
 
 1974 
 
 1.55 
 
 17.80 
 
 40.60 
 
 2.25 
 
 
 14 
 
 1975 
 
 2.15 
 
 16.40 
 
 48.20 
 
 3.32 
 
 
 15 
 
 1976 
 
 2.15 
 
 20.00 
 
 63.80 
 
 2.94 
 
 
 16 
 
 1977 
 
 4.14 
 
 20.50 
 
 42.20 
 
 4.40 
 
 
 17 
 
 1978 
 
 3.60 
 
 34.50 
 
 21.40 
 
 6.97 
 
 
 18 
 
 1979 
 
 4.12 
 
 20.00 
 
 50.80 
 
 3.80 
 
 17.33 
 
 19 
 
 1980 
 
 2.72 
 
 29.90 
 
 54.60 
 
 3.11 
 
 17.98 
 
 20 
 
 1981 
 
 6.42 
 
 26.50 
 
 61.60 
 
 2.98 
 
 44.55 
 
 21 
 
 1982 
 
 3.19 
 
 25.10 
 
 51.60 
 
 1.68 
 
 17.83 
 
 22 
 
 1983 
 
 J. 
 
 5 42 
 
 24.00 
 
 69.40 
 
 2.06 
 
 18.43 
 
 23 
 
 1984 
 
 3.47 
 
 23.00 
 
 54.00 
 
 4.13 
 
 28.28 
 
 24 
 
 1985 
 
 4.06 
 
 19.50 
 
 59.00 
 
 6.85 
 
 13.08 
 
 
 1986 
 
 5 18 
 
 28.80 
 
 57.00 
 
 11.42 
 
 12.11 
 
 26 
 
 1987 
 
 7.58 
 
 20.60 
 
 49.40 
 
 9.20 
 
 26.96 
 
 27 
 
 1988 
 
 3.36 
 
 15.60 
 
 58.00 
 
 5.41 
 
 29.39 
 
 28 
 
 1989 
 
 7.11 
 
 21.80 
 
 54.00 
 
 10.00 
 
 16.64 
 
 29 
 
 1990 
 
 6.33 
 
 16.60 
 
 67.00 
 
 25.98 
 
 10.06 
 
 30 
 
 1991 
 
 7.35 
 
 34.20 
 
 39.20 
 
 29.94 
 
 9.72 
 
 31 
 
 1992 
 
 8.63 
 
 23.80 
 
 56.60 
 
 51.88 
 
 14.76 
 
 32 
 
 1993 
 
 6.82 
 
 29.10 
 
 14.40 
 
 27.46 
 
 32.19 
 
 33 
 
 1994 
 
 5.10 
 
 41.00 
 
 8.80 
 
 39.41 
 
 19.87 
 
 34 
 
 1995 
 
 6.82 
 
 30.75 
 
 40.00 
 
 49.16 
 
 29.50 
 
 Notes: California producer marketing expenditures are reported on a California crop year (year ending October 31 of stated 
 year); source: annual reports of California Avocado Commission and Advisory Board. Florida prices and production quan- 
 tities are reported on a Florida crop year (year ending March 31 of stated year); source: USDA. Annual avocado imports are 
 reported as follows: 1951-76, year ending June 30 of stated year; 1977-88, year ending September 30 of stated year; 1989-95, 
 year ending October 31 of stated year; source: U.S. Bureau of the Census. Imports are reported for year ending October 31 
 of stated year; source: U.S. Bureau of the Census. 
 
 54 
 
Appendix Table 7. Monthly Sales and Average F.O.B. Prices for California Avocados 
 
 
 
 California 
 
 Ave. Ca 
 
 
 
 California 
 
 Ave. Ca 
 
 
 
 California 
 
 Ave. Ca 
 
 
 
 Quantity Sold fob Price 
 
 
 
 Quantity Sold 
 
 fob Price 
 
 
 
 
 
 Year 
 
 Mo. 
 
 pounds 
 
 cents/lb. 
 
 Year 
 
 Mo. 
 
 pounds 
 
 Cents/ 1L>. 
 
 Year 
 
 Mo. 
 
 
 cents /lb. 
 
 1984 
 
 11 
 
 
 
 1988 
 
 11 
 
 14,261,675 
 
 Ill 
 
 1992 
 
 11 
 
 9,765,125 
 
 72 
 
 1984 
 
 12 
 
 
 
 1988 
 
 12 
 
 21,646,825 
 
 68 
 
 1992 
 
 12 
 
 29,796,550 
 
 52 
 
 1985 
 
 1 
 
 
 
 1989 
 
 1 
 
 26,466,150 
 
 67 
 
 1993 
 
 1 
 
 29,999,375 
 
 53 
 
 1985 
 
 2 
 
 
 
 1989 
 
 2 
 
 24,169,100 
 
 67 
 
 1993 
 
 2 
 
 33,097,225 
 
 41 
 
 1985 
 
 3 
 
 
 
 1989 
 
 3 
 
 26,489,450 
 
 69 
 
 1993 
 
 3 
 
 45,231,200 
 
 35 
 
 1985 
 
 4 
 
 
 
 1989 
 
 4 
 
 25,954,025 
 
 67 
 
 1993 
 
 4 
 
 53,043,625 
 
 30 
 
 1985 
 
 5 
 
 
 
 1989 
 
 5 
 
 27,255,425 
 
 71 
 
 1993 
 
 5 
 
 46,445,925 
 
 28 
 
 1985 
 
 6 
 
 
 
 1989 
 
 6 
 
 19,063,375 
 
 85 
 
 1993 
 
 6 
 
 56,126,600 
 
 27 
 
 1985 
 
 7 
 
 
 
 1989 
 
 7 
 
 23,071,775 
 
 90 
 
 1993 
 
 7 
 
 50,041,825 
 
 29 
 
 1985 
 
 8 
 
 
 
 1989 
 
 8 
 
 18,808,225 
 
 89 
 
 1993 
 
 8 
 
 52,654,175 
 
 28 
 
 1985 
 
 9 
 
 
 
 1989 
 
 9 
 
 19,068,100 
 
 111 
 
 1993 
 
 9 
 
 36,741,000 
 
 46 
 
 1985 
 
 10 
 
 
 
 1989 
 
 10 
 
 14,883,725 
 
 128 
 
 1993 
 
 10 
 
 33,619,975 
 
 49 
 
 1985 
 
 11 
 
 
 
 1989 
 
 11 
 
 10,847,875 
 
 120 
 
 1993 
 
 11 
 
 25,716,900 
 
 72 
 
 1985 
 
 12 
 
 
 
 1989 
 
 12 
 
 17,456,950 
 
 102 
 
 1993 
 
 12 
 
 19,640,050 
 
 92 
 
 1986 
 
 1 
 
 
 
 1990 
 
 1 
 
 18,857,425 
 
 108 
 
 1994 
 
 1 
 
 24,332,325 
 
 91 
 
 1986 
 
 2 
 
 
 
 1990 
 
 2 
 
 16,076,675 
 
 105 
 
 1994 
 
 2 
 
 20,699,975 
 
 95 
 
 1986 
 
 3 
 
 
 
 1990 
 
 3 
 
 18,258,575 
 
 106 
 
 1994 
 
 3 
 
 24,922,325 
 
 101 
 
 1986 
 
 4 
 
 
 
 1990 
 
 4 
 
 17,741,625 
 
 135 
 
 1994 
 
 4 
 
 22,734,550 
 
 no 
 
 1986 
 
 5 
 
 
 
 1990 
 
 5 
 
 20,924,925 
 
 133 
 
 1994 
 
 5 
 
 23,904,300 
 
 114 
 
 1986 
 
 6 
 
 
 
 1990 
 
 6 
 
 18,517,400 
 
 156 
 
 1994 
 
 6 
 
 24,073,650 
 
 128 
 
 1986 
 
 7 
 
 
 
 1990 
 
 7 
 
 16,930,350 
 
 173 
 
 1994 
 
 7 
 
 21,221,875 
 
 135 
 
 1986 
 
 8 
 
 
 
 1990 
 
 8 
 
 14,993,625 
 
 173 
 
 1994 
 
 8 
 
 22,585,300 
 
 131 
 
 1986 
 
 9 
 
 
 
 1990 
 
 9 
 
 9,096,925 
 
 180 
 
 1994 
 
 9 
 
 13,891,500 
 
 136 
 
 1986 
 
 10 
 
 
 
 1990 
 
 10 
 
 5,539,200 
 
 181 
 
 1994 
 
 10 
 
 5,937,250 
 
 154 
 
 1986 
 
 11 
 
 99 "^^9 Ron 
 
 43 
 
 1990 
 
 11 
 
 8363,425 
 
 97 
 
 1994 
 
 11 
 
 4,970,625 
 
 129 
 
 1986 
 
 12 
 
 ■JO 680 975 
 
 32 
 
 1990 
 
 12 
 
 18,007,350 
 
 84 
 
 1994 
 
 12 
 
 12,800,625 
 
 88 
 
 1987 
 
 1 
 
 30,216,625 
 
 32 
 
 1991 
 
 1 
 
 17,739,875 
 
 95 
 
 1995 
 
 1 
 
 19,388,100 
 
 122 
 
 1987 
 
 2 
 
 31,147,725 
 
 32 
 
 1991 
 
 2 
 
 17,703,125 
 
 92 
 
 1995 
 
 2 
 
 19,998,200 
 
 97 
 
 1987 
 
 3 
 
 37,366,225 
 
 32 
 
 1991 
 
 3 
 
 17,977,200 
 
 99 
 
 1995 
 
 3 
 
 25,551,825 
 
 92 
 
 1987 
 
 4 
 
 ^^^^ 
 
 42,592,875 
 
 OA 
 
 30 
 
 1991 
 
 4 
 
 23,677,975 
 
 95 
 
 1995 
 
 4 
 
 26,143,725 
 
 82 
 
 1987 
 
 5 
 
 XX 720 925 
 
 27 
 
 1991 
 
 5 
 
 27,071,475 
 
 81 
 
 1995 
 
 5 
 
 31,796,875 
 
 74 . 
 
 1987 
 
 6 
 
 
 27 
 
 1991 
 
 6 
 
 22,899,850 
 
 92 
 
 1995 
 
 6 
 
 29,450,075 
 
 87 
 
 1987 
 
 7 
 
 097 70(1 
 
 26 
 
 1991 
 
 7 
 
 25,948,375 
 
 84 
 
 1995 
 
 7 
 
 29,761,100 
 
 83 
 
 1987 
 
 8 
 
 ATI f^'J COC 
 
 23 
 
 1991 
 
 8 
 
 25,059,475 
 
 75 
 
 1995 
 
 8 
 
 32,981,500 
 
 73 
 
 ■1987 
 
 9 
 
 
 24 
 
 1991 
 
 9 
 
 17,814,200 
 
 118 
 
 1995 
 
 9 
 
 21,466,200 
 
 118 
 
 1987 
 
 10 
 
 
 9R 
 
 1991 
 
 10 
 
 9,677,250 
 
 121 
 
 1995 
 
 10 
 
 14,688,950 
 
 130 
 
 1987 
 
 11 
 
 
 42 
 
 1991 
 
 11 
 
 3,131,925 
 
 121 
 
 1995 
 
 11 
 
 7,492,000 
 
 116 
 
 1987 
 
 12 
 
 
 38 
 
 1991 
 
 12 
 
 12,398,100 
 
 100 
 
 1995 
 
 12 
 
 19,977,450 
 
 102 
 
 1988 
 
 1 
 
 26,293,900 
 
 47 
 
 1992 
 
 1 
 
 25,475,225 
 
 76 
 
 1996 
 
 1 
 
 28,444,025 
 
 85. 
 
 1988 
 
 2 
 
 26,134,375 
 
 56 
 
 1992 
 
 2 
 
 21,803,950 
 
 69 
 
 1996 
 
 2 
 
 26,194,025 
 
 71 
 
 1988 
 
 3 
 
 27,361,775 
 
 65 
 
 1992 
 
 3 
 
 27,299,675 
 
 73 
 
 1996 
 
 3 
 
 29,788,075 
 
 73 
 
 1988 
 
 4 
 
 25,791,050 
 
 72 
 
 1992 
 
 4 
 
 33,337,525 
 
 59 
 
 1996 
 
 4 
 
 35,692,275 
 
 73 
 
 1988 
 
 5 
 
 26,078,975 
 
 82 
 
 1992 
 
 c 
 D 
 
 oo,oy/fD\J\J 
 
 DO 
 
 1996 
 
 5 
 
 35 891 450 
 
 65 
 
 1988 
 
 6 
 
 24,538,625 
 
 99 
 
 1992 
 
 6 
 
 35,265,700 
 
 61 
 
 1996 
 
 6 
 
 32,374,050 
 
 76 
 
 1988 
 
 7 
 
 24,%2,300 
 
 105 
 
 1992 
 
 7 
 
 27,778,900 
 
 89 
 
 1996 
 
 7 
 
 16,026,050 
 
 91 
 
 1988 
 
 8 
 
 20,934,525 
 
 121 
 
 1992 
 
 8 
 
 22,494,200 
 
 105 
 
 1996 
 
 8 
 
 
 
 1988 
 
 9 
 
 14,803,475 
 
 130 
 
 1992 
 
 9 
 
 17,970,675 
 
 113 
 
 1996 
 
 9 
 
 
 
 1988 
 
 10 
 
 11,766,525 
 
 139 
 
 1992 
 
 10 
 
 8,070,650 
 
 113 
 
 1996 
 
 10 
 
 
 
 Dots indicate missing or unavailable data. 
 Source: Calculated from AMRIC reports 
 
 55 
 
Appendix Table 8. Monthly Shipments of Florida and Imported Avocados 
 
 Year 
 
 Mo. 
 
 Avocado Shipments 
 Florida Imported 
 (lbs.) (lbs.) 
 
 Year 
 
 Mo. 
 
 Avocado Shipments 
 Florida Imported 
 (lbs.) (lbs.) 
 
 Year 
 
 Mo 
 
 Avocado Shipments 
 Florida Imported 
 (lbs.) (lbs.) 
 
 1984 
 
 11 
 
 8,916,850 
 
 
 1988 
 
 11 
 
 7,143,350 
 
 4,150,665 
 
 
 11 
 
 11 
 
 159,650 
 
 15,420,000 
 
 1984 
 
 12 
 
 6,217,100 
 
 
 
 19 
 
 5,722,100 
 
 1,384,278 
 
 
 
 129,600 
 
 2,970,000 
 
 1985 
 
 1 
 
 4 458 05(1 
 
 
 1989 
 
 1 
 
 0 Ana 1 nn 
 
 
 1993 
 
 1 
 
 83,050 
 
 1,180,000 
 
 1985 
 
 2 
 
 2,550,800 
 
 177,422 
 
 1989 
 
 2 
 
 978 100 
 
 0 
 
 1993 
 
 2 
 
 
 
 1985 
 
 3 
 
 443,800 
 
 37,845 
 
 1989 
 
 3 
 
 58,900 
 
 0 
 
 1993 
 
 3 
 
 7,400 
 
 140,000 
 
 1985 
 
 4 
 
 150 
 
 141,550 
 
 1989 
 
 4 
 
 0 
 
 20,000 
 
 1993 
 
 4 
 
 0 
 
 120,000 
 
 1985 
 
 5 
 
 0 
 
 127,922 
 
 1989 
 
 5 
 
 1,800 
 
 50,000 
 
 1993 
 
 5 
 
 0 
 
 210,000 
 
 1985 
 
 6 
 
 26,100 
 
 18,570 
 
 1989 
 
 6 
 
 248,800 
 
 50,000 
 
 1993 
 
 6 
 
 0 
 
 260,000 
 
 
 / 
 
 5,901,000 
 
 63,309 
 
 1989 
 
 7 
 
 7,253,050 
 
 300,000 
 
 1993 
 
 7 
 
 154,200 
 
 860,000 
 
 1985 
 
 8 
 
 9,422,200 
 
 363,903 
 
 1989 
 
 8 
 
 11,744,150 
 
 190,000 
 
 lyyo 
 
 Q 
 0 
 
 1,196,350 
 
 1,110,000 
 
 1985 
 
 9 
 
 9,152,900 
 
 352,606 
 
 1989 
 
 9 
 
 11,614,900 
 
 440,000 
 
 lyyo 
 
 Q 
 
 1,698,950 
 
 1,940,000 
 
 1985 
 
 10 
 
 10,266,250 
 
 384,180 
 
 1989 
 
 10 
 
 11,250,650 
 
 2,970,000 
 
 lyyo 
 
 in 
 
 1,579,450 
 
 2,940,000 
 
 1985 
 
 11 
 
 8,646,700 
 
 611,071 
 
 1989 
 
 11 
 
 9,464,150 
 
 4,220,000 
 
 lyyo 
 
 11 
 
 1,520,750 
 
 4,700,000 
 
 1985 
 
 12 
 
 5,871,200 
 
 1,083,781 
 
 1989 
 
 12 
 
 7,831,100 
 
 1,650,000 
 
 lyyo 
 
 iz 
 
 1,671,600 
 
 4,370,000 
 
 1986 
 
 1 
 
 4 019 800 
 
 5 252 149 
 
 1990 
 
 1 
 
 3 71 S 800 
 
 1 190 non 
 
 1994 
 
 1 
 
 A^A Ann 
 
 9 n7n nnn 
 
 1986 
 
 2 
 
 1 133 950 
 
 343 820 
 
 1990 
 
 2 
 
 1 657 800 
 
 180 000 
 
 1994 
 
 2 
 
 Z.OOf*T\J\J 
 
 AA{\ nnn 
 
 'irtxj f\J\j\j 
 
 1986 
 
 3 
 
 66,250 
 
 19,190 
 
 1990 
 
 3 
 
 207,750 
 
 150 000 
 
 1994 
 
 3 
 
 
 
 1986 
 
 4 
 
 0 
 
 263,310 
 
 1990 
 
 4 
 
 0 
 
 0 
 
 1994 
 
 4 
 
 0 
 
 150,000 
 
 1986 
 
 5 
 
 0 
 
 70,916 
 
 1990 
 
 5 
 
 0 
 
 20,000 
 
 1994 
 
 5 
 
 1,100 
 
 180,000 
 
 1986 
 
 6 
 
 2,750 
 
 64,430 
 
 1990 
 
 6 
 
 655,200 
 
 370,000 
 
 1994 
 
 6 
 
 464,350 
 
 580,000 
 
 1986 
 
 7 
 
 2,054,300 
 
 696,959 
 
 1990 
 
 7 
 
 5,771,100 
 
 600,000 
 
 1994 
 
 7 
 
 4,170,450 
 
 1,250,000 
 
 1986 
 
 8 
 
 6,647,250 
 
 1,154,974 
 
 1990 
 
 8 
 
 7,868,050 
 
 1,980,000 
 
 1994 
 
 8 
 
 7,038,050 
 
 1,430,000 
 
 1986 
 
 9 
 
 9,229,950 
 
 1,301,051 
 
 1990 
 
 9 
 
 5,444,150 
 
 6,210,000 
 
 1994 
 
 9 
 
 7,035,400 
 
 12,990,000 
 
 1986 
 
 10 
 
 9,498,000 
 
 4,079,963 
 
 1990 
 
 10 
 
 7,778,350 
 
 9,410,000 
 
 1994 
 
 10 
 
 6,581,800 
 
 11,020,000 
 
 1986 
 
 11 
 
 6,255,100 
 
 1,996,744 
 
 1990 
 
 11 
 
 5,042,050 
 
 7,170,000 
 
 1994 
 
 11 
 
 5,654,250 
 
 17,550,000 
 
 1986 
 
 12 
 
 6,142,850 
 
 556,288 
 
 1990 
 
 12 
 
 3,523,200 
 
 2,220,000 
 
 1994 
 
 12 
 
 4,630,600 
 
 4,890,000 
 
 
 
 
 1987 
 
 1 
 
 4,606,250 
 
 123,778 
 
 1991 
 
 1 
 
 1,144,400 
 
 1,670,000 
 
 1995 
 
 1 
 
 2,557,150 
 
 2,960,000 
 
 1987 
 
 2 
 
 2,682,000 
 
 14,695 
 
 1991 
 
 2 
 
 340,450 
 
 970,000 
 
 1995 
 
 2 
 
 796,800 
 
 1,100,000 
 
 1987 
 
 3 
 
 692,400 
 
 1,300 
 
 1991 
 
 3 
 
 24,000 
 
 10,000 
 
 1995 
 
 3 
 
 17,600 
 
 30,000 
 
 1987 
 
 4 
 
 0 
 
 14,556 
 
 1991 
 
 4 
 
 0 
 
 70,000 
 
 1995 
 
 4 
 
 0 
 
 100,000 
 
 1987 
 
 5 
 
 4,400 
 
 64,067 
 
 1991 
 
 5 
 
 5,350 
 
 30,000 
 
 1995 
 
 5 
 
 500 
 
 560,000 
 
 1987 
 
 6 
 
 37,250 
 
 1,006,731 
 
 1991 
 
 6 
 
 1,188,650 
 
 100,000 
 
 1995 
 
 6 
 
 148,950 
 
 840,000 
 
 1987 
 
 7 
 
 4,134,500 
 
 594,953 
 
 1991 
 
 7 
 
 8,773,700 
 
 340,000 
 
 1995 
 
 7 
 
 4,356,600 
 
 780,000 
 
 1987 
 
 8 
 
 7,920,600 
 
 393,048 
 
 1991 
 
 8 
 
 10,221,850 
 
 330,000 
 
 1995 
 
 8 
 
 7,634,500 
 
 580,000 
 
 1987 
 
 9 
 
 9,672,350 
 
 336,919 
 
 1991 
 
 9 
 
 9,041,800 
 
 2,410,000 
 
 1995 
 
 9 
 
 7,173,000 
 
 1,200,000 
 
 1987 
 
 10 
 
 8,498,100 
 
 162,589 
 
 1991 
 
 10 
 
 8,738,150 
 
 14,620,000 
 
 1995 
 
 10 
 
 6,931,300 
 
 18,580,000 
 
 1987 
 
 11 
 
 8,172,300 
 
 590,952 
 
 1991 
 
 11 
 
 6,847,200 
 
 8,270,000 
 
 1995 
 
 11 
 
 5,320,450 
 
 11,630,000 
 
 1987 
 
 12 
 
 7,188,400 
 
 674,968 
 
 1991 
 
 12 
 
 6,163,900 
 
 8,770,000 
 
 1995 
 
 12 
 
 3,266,850 
 
 
 1988 
 
 1 
 
 5,738,000 
 
 294,525 
 
 1992 
 
 1 
 
 3,584,900 
 
 2,360,000 
 
 1996 
 
 1 
 
 1,654,700 
 
 
 1988 
 
 2 
 
 3,712,650 
 
 13,825 
 
 1992 
 
 2 
 
 748,050 
 
 950,000 
 
 1996 
 
 2 
 
 548,100 
 
 
 1988 
 
 3 
 
 1,400,800 
 
 0 
 
 1992 
 
 3 
 
 187,200 
 
 200,000 
 
 1996 
 
 3 
 
 151,400 
 
 
 1988 
 
 4 
 
 4,600 
 
 28,187 
 
 1992 
 
 4 
 
 0 
 
 140,000 
 
 1996 
 
 4 
 
 
 
 1988 
 
 5 
 
 0 
 
 22,712 
 
 1992 
 
 5 
 
 1,450 
 
 150,000 
 
 1996 
 
 5 
 
 
 
 1988 
 
 6 
 
 240,650 
 
 12,366 
 
 1992 
 
 6 
 
 255,400 
 
 260,000 
 
 1996 
 
 6 
 
 
 
 1988 
 
 7 
 
 6,001,600 
 
 229,791 
 
 1992 
 
 7 
 
 6,396,800 
 
 720,000 
 
 1996 
 
 7 
 
 
 
 1988 
 
 8 
 
 10321,150 
 
 658,239 
 
 1992 
 
 8 
 
 6,724,850 
 
 740,000 
 
 1996 
 
 8 
 
 
 
 1988 
 
 9 
 
 9,879,650 
 
 2,722,730 
 
 1992 
 
 9 
 
 184,850 
 
 13,120,000 
 
 1996 
 
 9 
 
 
 
 1988 
 
 10 
 
 9,121,700 
 
 2,937,412 
 
 1992 
 
 10 
 
 200,900 
 
 16,200,000 
 
 1996 
 
 10 
 
 
 
 Soiirce: Florida data are from various annual reports of the Florida Avocado Administrative Committee. Import data are 
 from the U.S. Department of Commerce (via the USD A). 
 
 56 
 
Appendix Table 9. Macroeconomic Data Used in the Monthly Demand Analysis, California Avocado Crop 
 Years 1985-88 
 
 
 
 US Disposable 
 
 US 
 
 CPI 
 
 
 
 Price Indexes for Goods 
 
 
 
 
 Personal Income 
 
 Population 
 
 (1982-84=100) 
 
 
 Possibly Related to Avocados 
 
 
 Year 
 
 Mo. 
 
 (billions of dollars) 
 
 (thousands) 
 
 Quarterly 
 
 Monthly 
 
 Lettuce 
 
 
 Vegetables-1 Vegetables-2 
 
 1984 
 
 11 
 
 2,832.5 
 
 237,230 
 
 105.3 
 
 105.3 
 
 114.5 
 
 95.3 
 
 99.3 
 
 78.8 
 
 1984 
 
 12 
 
 2,832.5 
 
 237,230 
 
 105.3 
 
 105.3 
 
 90.2 
 
 90.2 
 
 96.1 
 
 73.5 
 
 
 1 
 1 
 
 2,916.2 
 
 237,673 
 
 106.0 
 
 105.5 
 
 126.0 
 
 92.4 
 
 105.8 
 
 91.3 
 
 
 2 
 
 2,916.2 
 
 237,673 
 
 106.0 
 
 106.0 
 
 111.0 
 
 109.7 
 
 112.9 
 
 110.3 
 
 i.yoD 
 
 3 
 
 2,916.2 
 
 237,673 
 
 106.0 
 
 106.4 
 
 100.2 
 
 125.2 
 
 111.5 
 
 117.4 
 
 1985 
 
 4 
 
 3,002.7 
 
 238,176 
 
 107.3 
 
 106.9 
 
 86.1 
 
 159.2 
 
 111.1 
 
 109.3 
 
 1985 
 
 5 
 
 3,002.7 
 
 238,176 
 
 107.3 
 
 107.3 
 
 96.5 
 
 90.4 
 
 102.5 
 
 88.3 
 
 1985 
 
 6 
 
 3,002.7 
 
 238,176 
 
 107.3 
 
 107.6 
 
 79.4 
 
 85.0 
 
 100.9 
 
 88.5 
 
 1985 
 
 7 
 
 3,013.8 
 
 238,789 
 
 108.0 
 
 107.8 
 
 97.2 
 
 90.2 
 
 103.7 
 
 124.8 
 
 1985 
 
 8 
 
 3,013.8 
 
 238,789 
 
 108.0 
 
 108.0 
 
 109.2 
 
 85.2 
 
 98.3 
 
 103.5 
 
 1985 
 
 9 
 
 3,013.8 
 
 238,789 
 
 108.0 
 
 108.3 
 
 113.8 
 
 83.1 
 
 93.5 
 
 92.1 
 
 1985 
 
 10 
 
 3,074.9 
 
 239,387 
 
 109.0 
 
 108.7 
 
 108.1 
 
 90.8 
 
 93.9 
 
 86.8 
 
 1985 
 
 11 
 
 3,074.9 
 
 239,387 
 
 109.0 
 
 109.0 
 
 103.2 
 
 107.5 
 
 97.8 
 
 84.4 
 
 1985 
 
 12 
 
 3,074.9 
 
 239,387 
 
 109.0 
 
 109.3 
 
 143.0 
 
 124.9 
 
 110.3 
 
 107.4 
 
 170D 
 
 
 3,139.5 
 
 239,861 
 
 109.2 
 
 109.6 
 
 145.1 
 
 144.9 
 
 118.1 
 
 107.2 
 
 
 2 
 
 3,139.5 
 
 239,861 
 
 109.2 
 
 109.3 
 
 99.2 
 
 104.3 
 
 101.4 
 
 82.7 
 
 170D 
 
 3 
 
 3,139.5 
 
 239,861 
 
 109.2 
 
 108.8 
 
 101.3 
 
 104.4 
 
 100.8 
 
 92.6 
 
 1986 
 
 4 
 
 3,170.7 
 
 240,368 
 
 109.0 
 
 108.6 
 
 128.3 
 
 108.5 
 
 108.8 
 
 116.5 
 
 1986 
 
 5 
 
 3,170.7 
 
 240,368 
 
 109.0 
 
 108.9 
 
 135.4 
 
 117.0 
 
 112.1 
 
 116.3 
 
 1986 
 
 6 
 
 3,170.7 
 
 240,368 
 
 109.0 
 
 109.5 
 
 106.8 
 
 108.3 
 
 106.4 
 
 91.0 
 
 1986 
 
 7 
 
 3,210.8 
 
 240,962 
 
 109.8 
 
 109.5 
 
 96.4 
 
 99.7 
 
 106.0 
 
 93.4 
 
 1986 
 
 8 
 
 3,210.8 
 
 240,962 
 
 109.8 
 
 109.7 
 
 98.3 
 
 94.9 
 
 105.0 
 
 90.1 
 
 1986 
 
 9 
 
 3,210.8 
 
 240,962 
 
 109.8 
 
 110.2 
 
 110.5 
 
 93.1 
 
 104.7 
 
 98.6 
 
 1986 
 
 10 
 
 3,229.1 
 
 241,539 
 
 110.4 
 
 110.3 
 
 108.2 
 
 111.8 
 
 107.2 
 
 99.5 
 
 1986 
 
 11 
 
 3,229.1 
 
 241,539 
 
 110.4 
 
 110.4 
 
 107.1 
 
 122.8 
 
 110.5 
 
 104.3 
 
 1986 
 
 12 
 
 3,229.1 
 
 241,539 
 
 110.4 
 
 110.5 
 
 115.1 
 
 126.2 
 
 111.7 
 
 100.4 
 
 1QR7 
 
 170/ 
 
 
 3,299.8 
 
 242,009 
 
 111.6 
 
 111.2 
 
 121.3 
 
 111.9 
 
 116.2 
 
 85.2 
 
 1QR7 
 170/ 
 
 2 
 
 3,299.8 
 
 242,009 
 
 111.6 
 
 111.6 
 
 117.7 
 
 113.4 
 
 123.2 
 
 91.9 
 
 1QR7 
 170/ 
 
 3 
 
 3,299.8 
 
 242,009 
 
 111.6 
 
 112.1 
 
 120.4 
 
 109.1 
 
 118.9 
 
 103.9 
 
 1987 
 
 4 
 
 3,298.5 
 
 242,520 
 
 113.1 
 
 112.7 
 
 121.0 
 
 119.1 
 
 123.7 
 
 WZ.Z 
 
 1987 
 
 5 
 
 3,298.5 
 
 242,520 
 
 113.1 
 
 113.1 
 
 99.7 
 
 114.6 
 
 123.6 
 
 94.4 
 
 1987 
 
 6 
 
 3,298.5 
 
 242,520 
 
 113.1 
 
 113.5 
 
 101.3 
 
 125.6 
 
 129.2 
 
 96.4 
 
 1987 
 
 7 
 
 3,382.3 
 
 243,120 
 
 114.4 
 
 113.8 
 
 115.5 
 
 116.6 
 
 121.0 
 
 101.9 
 
 1987 
 
 8 
 
 3,382.3 
 
 243,120 
 
 114.4 
 
 114.4 
 
 139.3 
 
 97.3 
 
 114.5 
 
 77.1 
 
 1987 
 
 9 
 
 3,382.3 
 
 243,120 
 
 114.4 
 
 115.0 
 
 142.5 
 
 103.7 
 
 114.6 
 
 98.2 
 
 1987 
 
 10 
 
 3,471.8 
 
 243,721 
 
 115.4 
 
 115.3 
 
 127.7 
 
 112.5 
 
 112.5 
 
 89.0 
 
 1987 
 
 11 
 
 3,471.8 
 
 243,721 
 
 115.4 
 
 115.4 
 
 158.1 
 
 138.2 
 
 121.2 
 
 135.4 
 
 1987 
 
 12 
 
 3,471.8 
 
 243,721 
 
 115.4 
 
 115.4 
 
 272.7 
 
 139.3 
 
 140.2 
 
 112.0 
 
 1988 
 
 
 3,549.6 
 
 244,208 
 
 116.1 
 
 115.7 
 
 277.6 
 
 123.2 
 
 143.9 
 
 135.9 
 
 1988 
 
 2 
 
 3,549.6 
 
 244,208 
 
 116.1 
 
 116.0 
 
 208.0 
 
 120.1 
 
 133.7 
 
 96.8 
 
 1988 
 
 3 
 
 3,549.6 
 
 244,208 
 
 116.1 
 
 116.5 
 
 150.0 
 
 108.9 
 
 125.6 
 
 95.8 
 
 1988 
 
 4 
 
 3,600.5 
 
 244,716 
 
 117.5 
 
 117.1 
 
 113.0 
 
 129.2 
 
 127.5 
 
 98.5 
 
 1988 
 
 5 
 
 3,600.5 
 
 244,716 
 
 117.5 
 
 117.5 
 
 118.5 
 
 123.5 
 
 124.5 
 
 88.5 
 
 1988 
 
 6 
 
 3,600.5 
 
 244,716 
 
 117.5 
 
 118.0 
 
 113.5 
 
 113.3 
 
 121.8 
 
 86.6 
 
 1988 
 
 7 
 
 3,674.9 
 
 245,354 
 
 119.1 
 
 118.5 
 
 113.8 
 
 123.4 
 
 127.0 
 
 96.9 
 
 1988 
 
 8 
 
 3,674.9 
 
 245,354 
 
 119.1 
 
 119.0 
 
 118.7 
 
 123.4 
 
 125.9 
 
 94.3 
 
 1988 
 
 9 
 
 3,674.9 
 
 245,354 
 
 119.1 
 
 119.8 
 
 134.2 
 
 129.8 
 
 13Z1 
 
 110.4 
 
 1988 
 
 10 
 
 3,738.4 
 
 245,966 
 
 120.3 
 
 120.2 
 
 135.1 
 
 128:7 
 
 129.4 
 
 101.0 
 
 Notes- Income and population are reported on a quarterly basis, income at a seasonally adjusted annual rate. CPI is the US 
 Consumer Price Index for all goods and all urban consumers. The Lettuce, Tomatoes, and Vegetables-1 variables are con- 
 sumer price indexes with 1982-84=1(X); the Vegetables-2 variable is a producer price index with 1982=100. Vegetables-1 
 includes potatoes, Vegetables-2 does not. . ^ r, 
 
 Sources: Income and Population data are from the Bureau of Economic Analysis. All price mdexes are from the Bureau ot 
 
 Labor Statistics. 
 
 57 
 
Appendix Table 9 (continued). Macroeconomic Data Used in the Monthly Demand Analysis, 
 
 California Avocado Crop Years 1985-88 
 
 
 
 US Disposable 
 
 US 
 
 CPI 
 
 
 
 Price Indexes for Goods 
 
 
 
 
 Personal Income 
 
 Population 
 
 (1982-84 
 
 =100) 
 
 
 Possibly Related to Avocados 
 
 Year 
 
 Mo. 
 
 (billions of dollars) 
 
 (thousands) 
 
 Quarterly 
 
 Monthly 
 
 Lettuce 
 
 Tomatoes 
 
 Vegetables-1 Vegetables-2 
 
 1988 
 
 11 
 
 3,738.4 
 
 245,966 
 
 120.3 
 
 120.3 
 
 126.8 
 
 129.2 
 
 126.7 
 
 103.8 
 
 1988 
 
 12 
 
 3,738.4 
 
 245,966 
 
 120.3 
 
 120.5 
 
 174.3 
 
 124.3 
 
 133.0 
 
 96.7 
 
 1989 
 
 1 
 
 3,828.3 
 
 246,460 
 
 121.7 
 
 121.1 
 
 179.7 
 
 121.7 
 
 141.4 
 
 93.4 
 
 1909 
 
 2 
 
 3,ozo.3 
 
 246,460 
 
 121.7 
 
 121.6 
 
 167.2 
 
 153.3 
 
 144.4 
 
 119.9 
 
 1989 
 
 3 
 
 3,828.3 
 
 246,460 
 
 121.7 
 
 122.3 
 
 150.7 
 
 132.1 
 
 140.2 
 
 111.0 
 
 1989 
 
 4 
 
 3,867.1 
 
 247,017 
 
 123.7 
 
 123.1 
 
 134.2 
 
 145.6 
 
 144.1 
 
 107.1 
 
 
 D 
 
 3,0D/.l 
 
 Z4/,U1/ 
 
 123.7 
 
 123.8 
 
 128.1 
 
 189.0 
 
 153.2 
 
 140.4 
 
 1989 
 
 6 
 
 3,867.1 
 
 247,017 
 
 123.7 
 
 124.1 
 
 149.1 
 
 131.6 
 
 150.8 
 
 117.0 
 
 1989 
 
 7 
 
 3,912.1 
 
 247,698 
 
 124.7 
 
 124.4 
 
 145.5 
 
 124.9 
 
 150.8 
 
 110.5 
 
 1989 
 
 8 
 
 3,912.1 
 
 247,698 
 
 124.7 
 
 124.6 
 
 146.5 
 
 119.3 
 
 145.1 
 
 96.3 
 
 1989 
 
 9 
 
 3,912.1 
 
 247,698 
 
 124.7 
 
 125.0 
 
 152.6 
 
 115.7 
 
 133.9 
 
 81.5 
 
 1989 
 
 10 
 
 3,970.3 
 
 248,374 
 
 125.9 
 
 125.6 
 
 160.4 
 
 126.2 
 
 134.8 
 
 101.0 
 
 1989 
 
 11 
 
 3,970.3 
 
 248,374 
 
 125.9 
 
 125.9 
 
 167.9 
 
 134.9 
 
 141.9 
 
 80.0 
 
 1989 
 
 12 
 
 3,970.3 
 
 248,374 
 
 125.9 
 
 126.1 
 
 135.8 
 
 140.3 
 
 136.5 
 
 88.4 
 
 
 1990 
 
 1 
 
 4,074.7 
 
 248,928 
 
 128.0 
 
 127.4 
 
 152.9 
 
 246.3 
 
 176.9 
 
 164.0 
 
 1990 
 
 2 
 
 4,074.7 
 
 248,928 
 
 128.0 
 
 128.0 
 
 134.2 
 
 321.8 
 
 186.3 
 
 203.2 
 
 1990 
 
 3 
 
 4,074.7 
 
 248,928 
 
 128.0 
 
 128.7 
 
 130.2 
 
 248.4 
 
 168.3 
 
 136.6 
 
 1990 
 
 4 
 
 4,143.3 
 
 249,564 
 
 129.3 
 
 128.9 
 
 137.1 
 
 117.6 
 
 145.6 
 
 74.8 
 
 1990 
 
 5 
 
 4,143.3 
 
 249,564 
 
 129.3 
 
 129.2 
 
 134.3 
 
 108.5 
 
 139.8 
 
 78.0 
 
 1990 
 
 6 
 
 4,143.3 
 
 249,564 
 
 129.3 
 
 129.9 
 
 120.2 
 
 126.1 
 
 140.0 
 
 83.7 
 
 1990 
 
 7 
 
 4,207.5 
 
 250,299 
 
 131.6 
 
 130.4 
 
 140.8 
 
 122.7 
 
 143.8 
 
 93.3 
 
 1990 
 
 8 
 
 4,207.5 
 
 250,299 
 
 131.6 
 
 131.6 
 
 142.4 
 
 122.0 
 
 139.8 
 
 79.0 
 
 1990 
 
 9 
 
 4,207.5 
 
 250,299 
 
 131.6 
 
 132.7 
 
 172.3 
 
 121.9 
 
 137.3 
 
 79.4 
 
 1990 
 
 10 
 
 4,241.4 
 
 251,031 
 
 133.7 
 
 133.5 
 
 192.8 
 
 133.2 
 
 142.2 
 
 96.2 
 
 1990 
 
 11 
 
 4,241.4 
 
 251,031 
 
 133.7 
 
 133.8 
 
 194.7 
 
 131.8 
 
 149.5 
 
 117.7 
 
 1990 
 
 12 
 
 4,241.4 
 
 251,031 
 
 133.7 
 
 133.8 
 
 152.0 
 
 129.5 
 
 144.0 
 
 87.2 
 
 
 1991 
 
 1 
 
 4,263.2 
 
 251,650 
 
 134.8 
 
 134.6 
 
 189.3 
 
 141.1 
 
 159.9 
 
 89.3 
 
 1991 
 
 2 
 
 4,263.2 
 
 251,650 
 
 134.8 
 
 134.8 
 
 160.9 
 
 131.6 
 
 152.5 
 
 87.3 
 
 1991 
 
 3 
 
 4,263.2 
 
 251,650 
 
 134.8 
 
 135.0 
 
 139.9 
 
 146.0 
 
 151.1 
 
 88.4 
 
 1991 
 
 4 
 
 4,329.6 
 
 252,295 
 
 135.6 
 
 135.2 
 
 154.0 
 
 181.3 
 
 169.2 
 
 112.8 
 
 1991 
 
 5 
 
 4,329.6 
 
 252,295 
 
 135.6 
 
 135.6 
 
 168.4 
 
 209.3 
 
 167.3 
 
 157.0 
 
 1991 
 
 6 
 
 4,329.6 
 
 252,295 
 
 135.6 
 
 136.0 
 
 180.8 
 
 243.2 
 
 180.5 
 
 138.0 
 
 1991 
 
 7 
 
 4,365.6 
 
 253,033 
 
 136.7 
 
 136.2 
 
 138.8 
 
 179.4 
 
 157.7 
 
 102.0 
 
 1991 
 
 8 
 
 4,365.6 
 
 253,033 
 
 136.7 
 
 136.6 
 
 133.8 
 
 120.4 
 
 142.2 
 
 82.6 
 
 1991 
 
 9 
 
 4,365.6 
 
 253,033 
 
 136.7 
 
 137.2 
 
 140.1 
 
 119.0 
 
 137.6 
 
 81.8 
 
 1991 
 
 10 
 
 4,416.4 
 
 253,743 
 
 137.7 
 
 137.4 
 
 139.7 
 
 113.5 
 
 134.0 
 
 73.5 
 
 1991 
 
 11 
 
 4,416.4 
 
 253,743 
 
 137.7 
 
 137.8 
 
 201.8 
 
 127.9 
 
 149.6 
 
 113.1 
 
 1991 
 
 12 
 
 4,416.4 
 
 253,743 
 
 137.7 
 
 137.9 
 
 170.1 
 
 124.5 
 
 150.7 
 
 76.1 
 
 
 1992 
 
 1 
 
 4,515.3 
 
 254,338 
 
 138.7 
 
 138.1 
 
 149.6 
 
 148.8 
 
 152.7 
 
 117.2 
 
 1992 
 
 2 
 
 4,515.3 
 
 254,338 
 
 138.7 
 
 138.6 
 
 13Z6 
 
 213.0 
 
 163.5 
 
 154.7 
 
 1992 
 
 3 
 
 4,515.3 
 
 254,338 
 
 138.7 
 
 139.3 
 
 141.1 
 
 261.6 
 
 172.7 
 
 147.9 
 
 1992 
 
 4 
 
 4,585.2 
 
 255,032 
 
 139.8 
 
 139.5 
 
 148.0 
 
 251.1 
 
 175.4 
 
 99.7 
 
 1992 
 
 5 
 
 4,585.2 
 
 255,032 
 
 139.8 
 
 139.7 
 
 149.6 
 
 133.0 
 
 149.6 
 
 89.9 
 
 1992 
 
 6 
 
 4,585.2 
 
 255,032 
 
 139.8 
 
 140.2 
 
 136.9 
 
 120.9 
 
 146.9 
 
 81.3 
 
 1992 
 
 7 
 
 4,613.9 
 
 255,815 
 
 140.9 
 
 140.5 
 
 135.3 
 
 126.6 
 
 148.1 
 
 85.5 
 
 1992 
 
 8 
 
 4,613.9 
 
 255,815 
 
 140.9 
 
 140.9 
 
 167.0 
 
 130.1 
 
 153.8 
 
 114.8 
 
 1992 
 
 9 
 
 4,613.9 
 
 255,815 
 
 140.9 
 
 141.3 
 
 192.5 
 
 125.5 
 
 152.8 
 
 114.8 
 
 1992 
 
 10 
 
 4,740.4 
 
 256,543 
 
 141.9 
 
 141.8 
 
 176.8 
 
 161.0 
 
 155.2 
 
 149.0 
 
 Notes: Income and population are reported on a quarterly basis, income at a seasonally adjusted annual rate. CPI is the US 
 Consumer Price Index for all goods and all urban consumers. The Lettuce, Tomatoes, and Vegetables-1 variables are con- 
 simier price indexes with 1982-84=100; the Vegetables-2 variable is a producer price index with 1982=100. Vegetables-1 
 includes potatoes, Vegetables-2 does not. 
 
 Sources: Income and Population data are from the Bvireau of Economic Analysis. AU price indexes are from the Bureau of 
 Labor Statistics. 
 
 58 
 
Appendix Table 9 (continued). Macroeconomic Data Used in the Monthly Demand Analysis, 
 
 California Avocado Crop Years 1985-88 
 
 US Disposable US CPI Price Indexes for Goods 
 
 Personal Income Population (1982-84=100) Possibly Related to Avocados 
 
 Year 
 
 Mo. 
 
 (billions of dollars) 
 
 (thousands) 
 
 Quarterly 
 
 Monthly 
 
 Lettuce 
 
 Tomatoes 
 
 Vegetables-1 Vegetables-2 
 
 1992 
 
 11 
 
 4,740.4 
 
 256343 
 
 141.9 
 
 142.0 
 
 156.2 
 
 196.1 
 
 158.4 
 
 108.2 
 
 1992 
 
 12 
 
 4,740.4 
 
 256,543 
 
 141.9 
 
 141.9 
 
 183.0 
 
 193.4 
 
 166.1 
 
 133.4 
 
 lyyi 
 
 I 
 
 4,t>o0.1 
 
 
 14^ 1 
 
 142.6 
 
 181.6 
 
 182.7 
 
 172.4 
 
 128.8 
 
 1993 
 
 2 
 
 4,686.1 
 
 257,155 
 
 143.1 
 
 143.1 
 
 187.3 
 
 170.9 
 
 171.1 
 
 125.8 
 
 1993 
 
 3 
 
 4,686.1 
 
 257,155 
 
 143.1 
 
 143.6 
 
 222.5 
 
 139.6 
 
 173.7 
 
 117.4 
 
 1993 
 
 4 
 
 4,771.6 
 
 257,787 
 
 144.2 
 
 144.0 
 
 213.1 
 
 159.2 
 
 179.3 
 
 178.5 
 
 1993 
 
 5 
 
 4,771.6 
 
 257,787 
 
 144.2 
 
 144.2 
 
 195.5 
 
 235.9 
 
 189.6 
 
 164.3 
 
 1993 
 
 6 
 
 4,771.6 
 
 257,787 
 
 144.2 
 
 144.4 
 
 142.2 
 
 193.2 
 
 167.1 
 
 80.7 
 
 1993 
 
 7 
 
 4,804.2 
 
 258,501 
 
 144.8 
 
 144.4 
 
 164.5 
 
 131.1 
 
 155.8 
 
 98.4 
 
 1993 
 
 8 
 
 4,804.2 
 
 258,501 
 
 144.8 
 
 144.8 
 
 173.8 
 
 134.2 
 
 156.1 
 
 110.5 
 
 1993 
 
 9 
 
 4,804.2 
 
 258,501 
 
 144.8 
 
 145.1 
 
 172.2 
 
 164.8 
 
 157.4 
 
 117.0 
 
 1993 
 
 10 
 
 4,895.4 
 
 259,192 
 
 145.8 
 
 145.7 
 
 168.1 
 
 147.7 
 
 157.7 
 
 89.5 
 
 1993 
 
 11 
 
 4,895.4 
 
 259,192 
 
 145.8 
 
 145.8 
 
 165.3 
 
 159.6 
 
 166.1 
 
 141.1 
 
 1993 
 
 12 
 
 4,895.4 
 
 259,192 
 
 145.8 
 
 145.8 
 
 15Z1 
 
 197.2 
 
 174.9 
 
 167.0 
 
 1994 
 
 1 
 
 4,800.0 
 
 
 7 
 
 IrtO./ 
 
 
 146.3 
 
 238.5 
 
 181.7 
 
 146.3 
 
 1994 
 
 2 
 
 4,856.8 
 
 259,738 
 
 146.7 
 
 146.7 
 
 146.5 
 
 175.1 
 
 168.1 
 
 99.3 
 
 1994 
 
 3 
 
 4,856.8 
 
 259,738 
 
 146.7 
 
 147.2 
 
 158.8 
 
 148.5 
 
 167.0 
 
 96.1 
 
 1994 
 
 4 
 
 5,002.2 
 
 260,327 
 
 147.6 
 
 147.4 
 
 144.9 
 
 150.7 
 
 163.9 
 
 91.4 
 
 1994 
 
 5 
 
 5,002.2 
 
 260,327 
 
 147.6 
 
 147.5 
 
 143.3 
 
 152.7 
 
 162.8 
 
 91.2 
 
 1994 
 
 6 
 
 5,002.2 
 
 260,327 
 
 147.6 
 
 148.0 
 
 147.6 
 
 170.0 
 
 168.7 
 
 94.9 
 
 1994 
 
 7 
 
 5,070.5 
 
 261,004 
 
 148.9 
 
 148.4 
 
 156.2 
 
 162.1 
 
 170.2 
 
 104.8 
 
 1994 
 
 8 
 
 5,070.5 
 
 261,004 
 
 148.9 
 
 149.0 
 
 157.3 
 
 159.2 
 
 163.7 
 
 95.7 
 
 1994 
 
 9 
 
 5,070.5 
 
 261,004 
 
 148.9 
 
 149.4 
 
 178.7 
 
 154.6 
 
 163.5 
 
 107.1 
 
 1994 
 
 10 
 
 5,145.7 
 
 261,653 
 
 149.6 
 
 149.5 
 
 178.8 
 
 158.1 
 
 167.0 
 
 113.8 
 
 1994 
 
 11 
 
 5,145.7 
 
 261,653 
 
 149.6 
 
 149.7 
 
 212.3 
 
 178.5 
 
 178.4 
 
 128.1 
 
 1994 
 
 12 
 
 5,145.7 
 
 261,653 
 
 149.6 
 
 149.7 
 
 273.4 
 
 233.6 
 
 212.7 
 
 244.7 
 
 1995 
 
 1 
 
 
 
 ISO 0 
 
 150.3 
 
 257.2 
 
 217.1 
 
 209.4 
 
 163.5 
 
 1995 
 
 2 
 
 5,225.5 
 
 262,181 
 
 150.9 
 
 150.9 
 
 176.1 
 
 217.2 
 
 198.6 
 
 149.2 
 
 1995 
 
 3 
 
 5,225.5 
 
 262,181 
 
 150.9 
 
 151.4 
 
 178.1 
 
 175.0 
 
 193.8 
 
 159.2 
 
 1995 
 
 4 
 
 5,260.5 
 
 262,748 
 
 152.2 
 
 151.9 
 
 379.6 
 
 202.3 
 
 220.4 
 
 199.1 
 
 1995 
 
 5 
 
 5;260.5 
 
 262,748 
 
 152.2 
 
 152.2 
 
 342.2 
 
 159.0 
 
 203.5 
 
 167.2 
 
 1995 
 
 6 
 
 5;260.5 
 
 262,748 
 
 152.2 
 
 152.5 
 
 209.5 
 
 178.2 
 
 194.9 
 
 127.2 
 
 1995 
 
 7 
 
 5,337.3 
 
 263,399 
 
 152.9 
 
 152.5 
 
 167.9 
 
 200.7 
 
 188.7 
 
 107.3 
 
 1995 
 
 8 
 
 5,337.3 
 
 263,399 
 
 152.9 
 
 152.9 
 
 177.5 
 
 150.9 
 
 175.4 
 
 94.8 
 
 1995 
 
 9 
 
 5,337.3 
 
 263,399 
 
 152.9 
 
 153.2 
 
 222.0 
 
 157.2 
 
 181.7 
 
 152.9 
 
 1995 
 
 10 
 
 5,406.6 
 
 264,032 
 
 153.6 
 
 153.7 
 
 193.1 
 
 175.7 
 
 182.0 
 
 116.0 
 
 1995 
 
 11 
 
 5,406.6 
 
 264,032 
 
 153.6 
 
 153.6 
 
 178.5 
 
 183.5 
 
 180.3 
 
 115.8 
 
 1995 
 
 12 
 
 5,406.6 
 
 264,032 
 
 153.6 
 
 153.5 
 
 172.2 
 
 242.6 
 
 188.4 
 
 125.5 
 
 1996 
 
 1 
 
 5,479.0 
 
 264,557 
 
 155.0 
 
 154.4 
 
 201.6 
 
 178.1 
 
 193.8 
 
 133.9 
 
 1996 
 
 2 
 
 5,479.0 
 
 264,557 
 
 155.0 
 
 154.9 
 
 165.6 
 
 178.0 
 
 188.4 
 
 119.4 
 
 1996 
 
 3 
 
 5,479.0 
 
 264,557 
 
 155.0 
 
 155.7 
 
 208.8 
 
 237.4 
 
 206.0 
 
 202.5 
 
 1996 
 
 4 
 
 
 
 156.5 
 
 156.3 
 
 189.3 
 
 292.3 
 
 209.2 
 
 155.6 
 
 1996 
 
 5 
 
 
 
 156.5 
 
 156.6 
 
 176.3 
 
 227.5 
 
 190.0 
 
 108.2 
 
 1996 
 
 6 
 
 
 
 156.5 
 
 156.7 
 
 183.4 
 
 190.3 
 
 188.0 
 
 96.6 
 
 1996 7 
 
 1996 8 
 
 1996 9 
 
 1996 10 
 
 Notes: Income and population are reported on a quarterly basis, income at a seasonally adjusted armual rate. CPI is the US 
 Consumer Price Index for all goods and all urban consumers. The Lettuce, Tomatoes, and Vegetables-1 variables are con- 
 sumer price indexes with 1982-84=100; the Vegetables-2 variable is a producer price index with 1982=100. Vegetables-1 
 includes potatoes, Vegetables-2 does not. 
 
 Sources: Income and Population data are from the Bureau of Economic Analysis. All price indexes are from the Bureau of 
 Labor Statistics. 
 
 59 
 
Appendix Table 10. Bearing Acreage of California Avocados: Actual and Simulated With and Without 
 Advertising, 1961-62 through 1994-95 
 
 Year 
 
 Actual 
 
 Estimated Bearing Acreage 
 
 
 Bearing Acreage 
 
 With Advertising 
 
 Without Advertising 
 
 1962 
 
 21,194 
 
 21,194 
 
 21,194 
 
 1963 
 
 21,921 
 
 ' 21,082 
 
 21,082 
 
 1964 
 
 21,574 
 
 20,702 
 
 20,698 
 
 1965 
 
 18,810 
 
 20,065 
 
 20,048 
 
 1966 
 
 18,620 
 
 19,228 
 
 19,196 
 
 1967 
 
 18,730 
 
 18,748 
 
 18,701 
 
 1968 
 
 19,220 
 
 18,145 
 
 18,063 
 
 1969 
 
 18,040 
 
 17,588 
 
 17,448 
 
 1970 
 
 18,380 
 
 17,731 
 
 17,486 
 
 1971 
 
 19,039 
 
 17,650 
 
 17,280 
 
 1972 
 
 19,611 
 
 18,425 
 
 17,852 
 
 1973 
 
 20,741 
 
 19,763 
 
 18,934 
 
 1974 
 
 20,715 
 
 22,194 
 
 21,037 
 
 1975 
 
 24,882 
 
 25,225 
 
 23,725 
 
 1976 
 
 29,041 
 
 30,384 
 
 28,530 
 
 1977 
 
 33 866 
 
 35,567 
 
 33,355 
 
 1978 
 
 39,802 
 
 41,458 
 
 38,801 
 
 1979 
 
 44,369 
 
 47,097 
 
 44,027 
 
 1980 
 
 47,831 
 
 53,091 
 
 49,609 
 
 1981 
 
 64,798 
 
 68,827 
 
 65,056 
 
 1982 
 
 69,448 
 
 72,950 
 
 68,929 
 
 1983 
 
 72,296 
 
 74,509 
 
 70,295 
 
 1984 
 
 72,861 
 
 75,779 
 
 71,446 
 
 1985 
 
 74,131 
 
 76,289 
 
 71,819 
 
 1986 
 
 74,812 
 
 76,158 
 
 71,635 
 
 1987 
 
 76,307 
 
 75,536 
 
 71,012 
 
 1988 
 
 75,062 
 
 74,918 
 
 70,389 
 
 1989 
 
 73 368 
 
 72,969 
 
 68,471 
 
 1990 
 
 71,007 
 
 71,508 
 
 67,140 
 
 1991 
 
 69,582 
 
 68,466 
 
 64,094 
 
 1992 
 
 68,159 
 
 67,172 
 
 62,634 
 
 1993 
 
 66,865 
 
 64,962 
 
 60,357 
 
 1994 
 
 61,254 
 
 62,526 
 
 57,914 
 
 1995 
 
 59,577 
 
 59,764 
 
 55,196 
 
 60 
 
Appendix Table 11a. Estimated Annual Short-Run Benefit/Cost Ratios From Avocado Advertising, 
 1961-62 to 1994-95 
 
 
 Short-Rian Impacts of Advertising 
 
 Year 
 
 
 
 TR Increase 
 
 CAC 
 
 TR increase 
 
 CAC Adv 
 
 Benefit/ 
 
 
 TRwith 
 
 TR w^ith no 
 
 From 
 
 Advertising 
 
 1994-95 
 
 1994-95 
 
 Cost 
 
 
 Advertising 
 
 Advertising 
 
 Advertising 
 
 
 dollars 
 
 dollars 
 
 Ratio 
 
 
 (million $) 
 
 (million $) 
 
 (million $) 
 
 (million $) 
 
 (million $) 
 
 (million $) 
 
 
 LyOZ. 
 
 10.89 
 
 10.75 
 
 0.15 
 
 0.18 
 
 0.74 
 
 0.91 
 
 0.81 
 
 1 QCi 
 lyoo 
 
 10.63 
 
 10.27 
 
 0.36 
 
 0.44 
 
 1.77 
 
 2.18 
 
 0.81 
 
 1704 
 
 12.28 
 
 11.81 
 
 0.47 
 
 0.51 
 
 2.32 
 
 2.49 
 
 0.93 
 
 lyOD 
 
 9.09 
 
 8.84 
 
 0.24 
 
 0.32 
 
 1.18 
 
 1.55 
 
 0.76 
 
 1700 
 
 17.88 
 
 16.84 
 
 1.04 
 
 0.79 
 
 4.89 
 
 3.70 
 
 1.32 
 
 lyo/ 
 
 20.86 
 
 19.70 
 
 1.16 
 
 0.78 
 
 5.30 
 
 3.56 
 
 1.49 
 
 lyoo 
 
 18.34 
 
 17.47 
 
 0.87 
 
 0.58 
 
 3.81 
 
 2.52 
 
 1.51 
 
 1 O^Q 
 
 iyoy 
 
 22.65 
 
 21.28 
 
 136 
 
 0.79 
 
 5.65 
 
 3.29 
 
 1.72 
 
 ly/u 
 
 21.61 
 
 20.19 
 
 1.^ 
 
 0.76 
 
 5.60 
 
 2.99 
 
 1.88 
 
 ly/ i 
 
 30.94 
 
 28.07 
 
 2S7 
 
 1.21 
 
 10.80 
 
 4.56 
 
 2.37 
 
 1 077 
 
 24.35 
 
 21.81 
 
 2.54 
 
 1.10 
 
 9.28 
 
 4.02 
 
 2.31 
 
 
 45.91 
 
 40.65 
 
 5.26 
 
 1.29 
 
 18.06 
 
 4.42 
 
 4.09 
 
 1 Q7A 
 
 51.53 
 
 44.66 
 
 6£7 
 
 1.55 
 
 21.24 
 
 4.78 
 
 4.44 
 
 17/0 
 
 63.14 
 
 53.70 
 
 9.44 
 
 2.15 
 
 26.74 
 
 6.10 
 
 439 
 
 l7/0 
 
 75.29 
 
 63.22 
 
 12.07 
 
 2.15 
 
 32.33 
 
 5.76 
 
 5.61 
 
 1 Q77 
 17/ / 
 
 90.52 
 
 71.60 
 
 18.92 
 
 4.14 
 
 47.58 
 
 10.40 
 
 4.58 
 
 1 Q7Q 
 
 117.95 
 
 94.93 
 
 23.02 
 
 3.60 
 
 53.80 
 
 8.41 
 
 6.40 
 
 1 Q7Q 
 
 i7/y 
 
 125.97 
 
 100.94 
 
 25.03 
 
 4.12 
 
 52.54 
 
 8.66 
 
 6.07 
 
 LyOU 
 
 136.59 
 
 114.32 
 
 22.28 
 
 2.72 
 
 41.21 
 
 5.02 
 
 8.21 
 
 1 QSI 
 
 109.89 
 
 89.01 
 
 20.88 
 
 6.42 
 
 35.01 
 
 10.77 
 
 3.25 
 
 1 QR9 
 i70Z 
 
 130.17 
 
 111.06 
 
 19.12 
 
 3.19 
 
 30.19 
 
 5.04 
 
 5.99 
 
 lyoJ 
 
 113.74 
 
 92.65 
 
 21.09 
 
 5.42 
 
 32.27 
 
 8.29 
 
 3.89 
 
 1 QRA 
 
 105.33 
 
 89.24 
 
 16.10 
 
 3.47 
 
 23.61 
 
 5.09 
 
 4.64 
 
 lyoD 
 
 146.00 
 
 120.57 
 
 25.43 
 
 4.06 
 
 36.01 
 
 S.75 
 
 6.26 
 
 lyoo 
 
 197.36 
 
 156.56 
 
 40.80 
 
 5.18 
 
 56.73 
 
 721 
 
 7.87 
 
 lyo/ 
 
 133.75 
 
 103.94 
 
 29.80 
 
 7.58 
 
 39.98 
 
 10.17 
 
 3.93 
 
 1 noo 
 
 19bo 
 
 232.39 
 
 190.42 
 
 41.97 
 
 3.36 
 
 54.06 
 
 433 
 
 12.48 
 
 i9oy 
 
 292.56 
 
 220.77 
 
 71.79 
 
 7.11 
 
 Oo.Zo 
 
 ft 
 
 in in 
 
 1990 
 
 311.27 
 
 235.68 
 
 75.59 
 
 6.33 
 
 88.14 
 
 738 
 
 11.95 
 
 1991 
 
 285.71 
 
 213.37 
 
 72.34 
 
 7.35 
 
 80.94 
 
 
 9.84 
 
 1992 
 
 224.14 
 
 164.63 
 
 59.51 
 
 8.63 
 
 64.64 
 
 937 
 
 6.90 
 
 1993 
 
 170.51 
 
 130.37 
 
 40.14 
 
 6.82 
 
 42.33 
 
 7.19 
 
 5.89 
 
 1994 
 
 299.58 
 
 226.27 
 
 73.31 
 
 5.10 
 
 75.39 
 
 5J2S 
 
 14.37 
 
 1995 
 
 290.45 
 
 211.69 
 
 78.76 
 
 6.82 
 
 78.76 
 
 6.82 
 
 11.56 
 
 61 
 
Appendix Table lib. Estimated Long-Run Benefit/Cost Ratios From Avocado Advertising, 1961-62 to 1994-95 
 
 
 Long-Run Impacts of Advertising 
 
 Year 
 
 JD b IXll la IcU. 
 
 J^bllllldieLl 
 
 1 IS. increase 
 
 
 TR increase 
 
 L.At_ Adv 
 
 Deneiit/ 
 
 
 TRwith 
 
 TR with no 
 
 From 
 
 Advertising 
 
 1994-95 
 
 1994-95 
 
 Cost 
 
 
 Advertising 
 
 Advertising 
 
 Advertising 
 
 
 dollars 
 
 dollars 
 
 Ratio 
 
 
 (million $) 
 
 (million $) 
 
 (million $) 
 
 (million $) 
 
 (million $) 
 
 (million $) 
 
 
 1962 
 
 10.78 
 
 10.64 
 
 0.15 
 
 0.18 
 
 0.73 
 
 0.91 
 
 0.80 
 
 1963 
 
 10.66 
 
 10.30 
 
 0.36 
 
 0.44 
 
 1.78 
 
 2.18 
 
 0.81 
 
 1964 
 
 12.72 
 
 12.22 
 
 0.49 
 
 0.51 
 
 TAl 
 
 2.49 
 
 0.97 
 
 1965 
 
 9.30 
 
 9.06 
 
 0.25 
 
 0.32 
 
 121 
 
 155 
 
 0.78 
 
 1966 
 
 17.60 
 
 16.60 
 
 1.00 
 
 0.79 
 
 4.70 
 
 3.70 
 
 1.27 
 
 1967 
 
 20.75 
 
 19.65 
 
 1.11 
 
 0.78 
 
 5.06 
 
 356 
 
 1.42 
 
 1968 
 
 18.66 
 
 17.81 
 
 0.85 
 
 0.58 
 
 3.71 
 
 2J2 
 
 1.47 
 
 1969 
 
 24.26 
 
 22.92 
 
 1.34 
 
 0.79 
 
 5.1^ 
 
 329 
 
 1.69 
 
 1970 
 
 21.84 
 
 20.53 
 
 131 
 
 0.76 
 
 5.15 
 
 2.99 
 
 1.72 
 
 1971 
 
 32.27 
 
 29.78 
 
 2.49 
 
 1.21 
 
 9.38 
 
 4J6 
 
 2.05 
 
 1972 
 
 24.88 
 
 22.55 
 
 2.33 
 
 1.10 
 
 8.49 
 
 4(E 
 
 2.11 
 
 1973 
 
 46.04 
 
 42.48 
 
 3.56 
 
 1.29 
 
 12.23 
 
 4^ 
 
 2.77 
 
 19/4 
 
 47.28 
 
 42.94 
 
 4.35 
 
 1.55 
 
 13.43 
 
 4.78 
 
 2.81 
 
 1975 
 
 50.09 
 
 46.78 
 
 3.30 
 
 2.15 
 
 9.36 
 
 6.10 
 
 1.54 
 
 1976 
 
 61.56 
 
 55.43 
 
 6.13 
 
 2.15 
 
 16.42 
 
 5.76 
 
 2.85 
 
 1977 
 
 55.46 
 
 49.99 
 
 5.47 
 
 4.14 
 
 13.75 
 
 10.40 
 
 1.32 
 
 1978 
 
 70.69 
 
 64.87 
 
 5M 
 
 3.60 
 
 13.59 
 
 8.41 
 
 1.62 
 
 1979 
 
 76.75 
 
 70.95 
 
 5J1 
 
 4.12 
 
 12.19 
 
 8.66 
 
 1.41 
 
 1980 
 
 99.95 
 
 92.79 
 
 7M 
 
 2.72 
 
 13.24 
 
 5.02 
 
 2.64 
 
 1981 
 
 40.40 
 
 39.43 
 
 0.f7 
 
 6.42 
 
 1.62 
 
 10.77 
 
 0.15 
 
 1982 
 
 88.32 
 
 86.15 
 
 %16 
 
 3.19 
 
 3.42 
 
 5.04 
 
 0.68 
 
 1983 
 
 89.31 
 
 84.63 
 
 4.68 
 
 5.42 
 
 7.16 
 
 829 
 
 0.86 
 
 1984 
 
 86.10 
 
 86.46 
 
 -036 
 
 3.47 
 
 -0.53 
 
 5.09 
 
 -0.10 
 
 1985 
 
 118.31 
 
 115.41 
 
 im 
 
 4.06 
 
 4.12 
 
 5.75 
 
 0.72 
 
 1986 
 
 162.99 
 
 151.65 
 
 11J4 
 
 5.18 
 
 15.77 
 
 7.21 
 
 2.19 
 
 1987 
 
 109.10 
 
 103.89 
 
 5.21 
 
 7.58 
 
 6.99 
 
 10.17 
 
 0.69 
 
 1988 
 
 196.59 
 
 192.77 
 
 3.S2 
 
 3.36 
 
 4.92 
 
 4.33 
 
 1.13 
 
 1989 
 
 247.17 
 
 223.14 
 
 24.02 
 
 7.11 
 
 29.53 
 
 8.74 
 
 3.38 
 
 1990 
 
 254.74 
 
 224.60 
 
 30.13 
 
 6.33 
 
 35.14 
 
 7M 
 
 4.76 
 
 1991 
 
 235.73 
 
 210.06 
 
 25.67 
 
 7.35 
 
 28.73 
 
 823 
 
 3.49 ■ 
 
 1992 
 
 183.81 
 
 163.68 
 
 20.13 
 
 8.63 
 
 21.87 
 
 9.37 
 
 2.33 
 
 1993 
 
 146.31 
 
 144.76 
 
 1.55 
 
 6.82 
 
 1.63 
 
 7.19 
 
 0.23 
 
 1994 
 
 251.50 
 
 235.14 
 
 16.35 
 
 5.10 
 
 16.82 
 
 52S 
 
 3.20 
 
 1995 
 
 228.36 
 
 211.42 
 
 16.95 
 
 6.82 
 
 16.95 
 
 6.82 
 
 2.49 
 
 62 
 
Appendix Table 11c. Estimated Long-Run Benefit/ Cost Ratios From Avocado Advertising for the Producers' 
 Share of Costs, 1961-62 to 1994-95 
 
 
 Long-Run Impacts of Advertising 
 
 Crop 
 Year Ending 
 
 TR Increase 
 From Adv 
 
 1994-95 base 
 (million $) 
 
 CAC Adv 
 Costs 
 1994-95 base 
 (million $) 
 
 Producers' 
 Share of Costs 
 1994-95 base 
 (million $) 
 
 Benefit/ 
 Cost Ratio 
 
 1962 
 
 0.7329 
 
 0.9129 
 
 0.9129 
 
 0.80 
 
 1963 
 
 1.7769 
 
 2.1848 
 
 2.1848 
 
 0.81 
 
 1964 
 
 2.4141 
 
 2.4892 
 
 2.4892 
 
 0.97 
 
 lyoD 
 
 1.2081 
 
 1.5525 
 
 1.5525 
 
 0.78 
 
 lyoo 
 
 4.6989 
 
 3.6952 
 
 3.6952 
 
 1.27 
 
 iyo/ 
 
 5.0589 
 
 3.5637 
 
 3.5637 
 
 1.42 
 
 
 3.7120 
 
 2.5199 
 
 2.5502 
 
 1.46 
 
 1969 
 
 5.5716 
 
 3.2876 
 
 3.3753 
 
 1.65 
 
 19/U 
 
 5.1487 
 
 2.9861 
 
 3.3622 
 
 1.53 
 
 1971 
 
 9.3763 
 
 4.5649 
 
 4.8225 
 
 1.94 
 
 1972 
 
 8.4901 
 
 4.0169 
 
 5.2581 
 
 1,61 
 
 1973 
 
 12.2311 
 
 4.4189 
 
 4.5892 
 
 2.67 
 
 1974 
 
 13.4347 
 
 4.7809 
 
 5.6285 
 
 2J9 
 
 1 OTC 
 
 ly/D 
 
 9.3594 
 
 6.0961 
 
 3.7529 
 
 Z49 
 
 ly/t) 
 
 16.4241 
 
 5.7620 
 
 5.8632 
 
 2.80 
 
 19// 
 
 13.7537 
 
 10.3991 
 
 6.7154 
 
 2,05 
 
 1 d'ye 
 19/0 
 
 13.5874 
 
 8.4052 
 
 4.5256 
 
 3.00 
 
 19/9 
 
 12.1912 
 
 8.6551 
 
 4.4918 
 
 2.71 
 
 19oU 
 
 13.2441 
 
 5.0219 
 
 3.8131 
 
 ^ 3.47 
 
 1901 
 
 1.6197 
 
 10.7710 
 
 8.2321 
 
 OJO 
 
 19oZ 
 
 3.4167 
 
 5.0414 
 
 2.4797 
 
 1.38 
 
 
 7.1584 
 
 8.2908 
 
 5.3306 
 
 1.34 
 
 19o4 
 
 -0.5275 
 
 5.0941 
 
 1.9655 
 
 -0.27 
 
 1985 
 
 4.1161 
 
 5.7528 
 
 2.4705 
 
 1.67 
 
 1986 
 
 15.7686 
 
 7.2072 
 
 3.9103 
 
 4.03 
 
 1987 
 
 6.9890 
 
 10.1685 
 
 6.3259 
 
 1.10 
 
 1988 
 
 4.9164 
 
 4.3332 
 
 0.2258 
 
 21.77 
 
 1989 
 
 29.5267 
 
 8.7354 
 
 4.1010 
 
 7.20 
 
 1990 
 
 35.1371 
 
 7.3753 
 
 5.9987 
 
 5M 
 
 1991 
 
 28.7269 
 
 8.2259 
 
 5.9336 
 
 4.84 
 
 1992 
 
 21.8671 
 
 9.3726 
 
 7.5513 
 
 2.90 
 
 1993 
 
 1.6310 
 
 7.1887 
 
 3.8668 
 
 0.42 
 
 1994 
 
 16.8167 
 
 5.2479 
 
 3.5628 
 
 4.72 
 
 1995 
 
 16.9453 
 
 6.8152 
 
 4.6479 
 
 3.65 
 
 63 
 
Appendix Table lid. Projected Long-Run Benefit/Cost Ratios From Avocado Advertising, Producers Pay All 
 Costs and Producers Share Costs, 1995-96 to 2014-15 
 
 Projected Long-Run Impacts of Advertising 
 
 Crop 
 Year Ending 
 
 Total Crop Revenue 
 
 Increased 
 Rev from 
 
 Adv 
 (million $) 
 
 Total 
 Adv 
 Cost 
 (miUion $) 
 
 Producers' 
 Share of Adv 
 Costs 
 (million $) 
 
 Benefit/ Cost Ratios 
 
 With AHv 
 
 V V ILL I V 
 
 (million $) 
 
 (million $) 
 
 pay all costs 
 
 1 roaucers 
 share costs 
 
 1996 
 
 91 7 ni 
 
 ^x/ <oi 
 
 9nA Q9 
 
 10.09 
 
 4.47 
 
 1.72 
 
 9 9A 
 Z.Zo 
 
 O.oO 
 
 1997 
 
 
 91 9 flQ 
 
 9.58 
 
 4.47 
 
 1.86 
 
 9 14 
 
 K 1A 
 
 1998 
 
 998 79 
 
 910 R1 
 
 9.21 
 
 447 
 
 urn 
 
 9 
 
 Z.UD 
 
 n9 
 
 1999 
 
 937 86 
 
 998 
 
 932 
 
 447 
 
 1.74 
 
 9 OR 
 
 
 2000 
 
 947 ^9 
 
 937 61 
 
 9.71 
 
 447 
 
 1.67 
 
 9 17 
 
 
 2001 
 
 2'i6 86 
 
 946 56 
 
 10.30 
 
 4.47 
 
 1.60 
 
 9 '^0 
 
 
 2002 
 
 
 9^4 70 
 
 11.13 
 
 447 
 
 1.65 
 
 9 4Q 
 
 A 7=1 
 
 2003 
 
 ^/ J./O 
 
 lf<\ Q4 
 
 11.82 
 
 447 
 
 1.77 
 
 9 A4 
 
 D.OD 
 
 2004 
 
 
 
 12.76 
 
 447 
 
 1.91 
 
 Z.OC7 
 
 A AO 
 D.D" 
 
 2005 
 
 9Q9 '^7 
 
 97c 40 
 
 13.97 
 
 447 
 
 2.07 
 
 ■^19 
 
 A 74 
 
 2006 
 
 ■^09 49 
 
 987 15 
 
 15.28 
 
 4M 
 
 2.24 
 
 49 
 
 6 89 
 
 2007 
 
 312.53 
 
 295.93 
 
 16.60 
 
 4.47 
 
 2.41 
 
 3.71 
 
 6.88 
 
 2008 
 
 322.63 
 
 304.74 
 
 17.89 
 
 447 
 
 2.59 
 
 4.00 
 
 6.90 
 
 2009 
 
 332.85 
 
 313.76 
 
 19.09 
 
 
 2.76 
 
 4.27 
 
 6.91 
 
 2010 
 
 343.30 
 
 323.13 
 
 20.17 
 
 447 
 
 2.91 
 
 4.51 
 
 6.92 
 
 2011 
 
 354.04 
 
 332.94 
 
 21.11 
 
 447 
 
 3.03 
 
 4.72 
 
 6.96 
 
 2012 
 
 365.11 
 
 343.24 
 
 21.87 
 
 447 
 
 3.11 
 
 4.89 
 
 7.04 
 
 2013 
 
 376.45 
 
 354.01 
 
 22.44 
 
 447 
 
 3.14 
 
 5.02 
 
 7.14 
 
 2014 
 
 388.08 
 
 365.28 
 
 22.81 
 
 447 
 
 3.13 
 
 5.10 
 
 7.28 
 
 2015 
 
 400.05 
 
 377.08 
 
 22.97 
 
 4.47 
 
 3.08 
 
 5.14 
 
 7.45 
 
 Note: For this projection we used recent values for advertising ($4.47 million annually), imports (.18685 pounds per capita), 
 Florida production (.1534 pounds per capita), consumer income (increased 1 percent annually), population (increased 1 
 percent annually), and average per acre yields (6418 pounds per acre). 
 
 64 
 
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